diff --git a/src/BayesNet/BayesMetrics.cc b/src/BayesNet/BayesMetrics.cc
index 6671995..75b83ef 100644
--- a/src/BayesNet/BayesMetrics.cc
+++ b/src/BayesNet/BayesMetrics.cc
@@ -12,8 +12,8 @@ namespace bayesnet {
         : features(features)
         , className(className)
         , classNumStates(classNumStates)
+        , samples(torch::zeros({ static_cast<int>(vsamples[0].size()), static_cast<int>(vsamples.size() + 1) }, torch::kInt32))
     {
-        samples = torch::zeros({ static_cast<int>(vsamples[0].size()), static_cast<int>(vsamples.size() + 1) }, torch::kInt32);
         for (int i = 0; i < vsamples.size(); ++i) {
             samples.index_put_({ "...", i }, torch::tensor(vsamples[i], torch::kInt32));
         }
@@ -67,6 +67,7 @@ namespace bayesnet {
         }
         return matrix;
     }
+    // To Interface with Python
     vector<float> Metrics::conditionalEdgeWeights()
     {
         auto matrix = conditionalEdge();
@@ -123,7 +124,6 @@ namespace bayesnet {
     */
     vector<pair<int, int>> Metrics::maximumSpanningTree(vector<string> features, Tensor& weights, int root)
     {
-        auto result = vector<pair<int, int>>();
         auto mst = MST(features, weights, root);
         return mst.maximumSpanningTree();
     }
diff --git a/src/BayesNet/Classifier.cc b/src/BayesNet/Classifier.cc
index d77c2c8..0064f66 100644
--- a/src/BayesNet/Classifier.cc
+++ b/src/BayesNet/Classifier.cc
@@ -4,7 +4,7 @@
 namespace bayesnet {
     using namespace torch;
 
-    Classifier::Classifier(Network model) : model(model), m(0), n(0), metrics(Metrics()), fitted(false) {}
+    Classifier::Classifier(const Network& model) : model(model), m(0), n(0), metrics(Metrics()), fitted(false) {}
     Classifier& Classifier::build(vector<string>& features, string className, map<string, vector<int>>& states)
     {
         dataset = torch::cat({ X, y.view({y.size(0), 1}) }, 1);
@@ -125,7 +125,6 @@ namespace bayesnet {
     }
     void Classifier::addNodes()
     {
-        auto test = model.getEdges();
         // Add all nodes to the network
         for (auto feature : features) {
             model.addNode(feature, states[feature].size());
diff --git a/src/BayesNet/Classifier.h b/src/BayesNet/Classifier.h
index ad56336..a9ae764 100644
--- a/src/BayesNet/Classifier.h
+++ b/src/BayesNet/Classifier.h
@@ -27,7 +27,7 @@ namespace bayesnet {
         void checkFitParameters();
         virtual void train() = 0;
     public:
-        Classifier(Network model);
+        Classifier(const Network& model);
         virtual ~Classifier() = default;
         Classifier& fit(vector<vector<int>>& X, vector<int>& y, vector<string>& features, string className, map<string, vector<int>>& states) override;
         Classifier& fit(torch::Tensor& X, torch::Tensor& y, vector<string>& features, string className, map<string, vector<int>>& states) override;
diff --git a/src/BayesNet/Ensemble.cc b/src/BayesNet/Ensemble.cc
index 8aa2518..dce0d3d 100644
--- a/src/BayesNet/Ensemble.cc
+++ b/src/BayesNet/Ensemble.cc
@@ -148,10 +148,10 @@ namespace bayesnet {
     }
     int Ensemble::getNumberOfStates()
     {
-        int states = 0;
+        int nstates = 0;
         for (auto i = 0; i < n_models; ++i) {
-            states += models[i]->getNumberOfStates();
+            nstates += models[i]->getNumberOfStates();
         }
-        return states;
+        return nstates;
     }
 }
\ No newline at end of file
diff --git a/src/BayesNet/KDB.h b/src/BayesNet/KDB.h
index 9683955..d8045c3 100644
--- a/src/BayesNet/KDB.h
+++ b/src/BayesNet/KDB.h
@@ -13,7 +13,7 @@ namespace bayesnet {
     protected:
         void train() override;
     public:
-        KDB(int k, float theta = 0.03);
+        explicit KDB(int k, float theta = 0.03);
         vector<string> graph(string name = "KDB") override;
     };
 }
diff --git a/src/BayesNet/Mst.cc b/src/BayesNet/Mst.cc
index b86812b..36c2fd0 100644
--- a/src/BayesNet/Mst.cc
+++ b/src/BayesNet/Mst.cc
@@ -7,9 +7,8 @@
 
 namespace bayesnet {
     using namespace std;
-    Graph::Graph(int V)
+    Graph::Graph(int V) : V(V), parent{ vector<int>(V) }
     {
-        parent = vector<int>(V);
         for (int i = 0; i < V; i++)
             parent[i] = i;
         G.clear();
@@ -34,10 +33,11 @@ namespace bayesnet {
     }
     void Graph::kruskal_algorithm()
     {
-        int i, uSt, vEd;
+        int i;
         // sort the edges ordered on decreasing weight
-        sort(G.begin(), G.end(), [](auto& left, auto& right) {return left.first > right.first;});
+        sort(G.begin(), G.end(), [](const auto& left, const auto& right) {return left.first > right.first;});
         for (i = 0; i < G.size(); i++) {
+            int uSt, vEd;
             uSt = find_set(G[i].second.first);
             vEd = find_set(G[i].second.second);
             if (uSt != vEd) {
diff --git a/src/BayesNet/Mst.h b/src/BayesNet/Mst.h
index 15b0dbb..e257b40 100644
--- a/src/BayesNet/Mst.h
+++ b/src/BayesNet/Mst.h
@@ -12,7 +12,6 @@ namespace bayesnet {
         vector<string> features;
         int root;
     public:
-        MST() = default;
         MST(vector<string>& features, Tensor& weights, int root);
         vector<pair<int, int>> maximumSpanningTree();
     };
@@ -23,7 +22,7 @@ namespace bayesnet {
         vector <pair<float, pair<int, int>>> T; // vector for mst
         vector<int> parent;
     public:
-        Graph(int V);
+        explicit Graph(int V);
         void addEdge(int u, int v, float wt);
         int find_set(int i);
         void union_set(int u, int v);
diff --git a/src/BayesNet/Network.cc b/src/BayesNet/Network.cc
index eb3ffeb..7c0572a 100644
--- a/src/BayesNet/Network.cc
+++ b/src/BayesNet/Network.cc
@@ -4,11 +4,11 @@
 #include "bayesnetUtils.h"
 namespace bayesnet {
     Network::Network() : laplaceSmoothing(1), features(vector<string>()), className(""), classNumStates(0), maxThreads(0.8), fitted(false) {}
-    Network::Network(float maxT) : laplaceSmoothing(1), features(vector<string>()), className(""), classNumStates(0), maxThreads(maxT), fitted(false) {}
-    Network::Network(float maxT, int smoothing) : laplaceSmoothing(smoothing), features(vector<string>()), className(""), classNumStates(0), maxThreads(maxT), fitted(false) {}
-    Network::Network(Network& other) : laplaceSmoothing(other.laplaceSmoothing), features(other.features), className(other.className), classNumStates(other.getClassNumStates()), maxThreads(other.getmaxThreads()), fitted(other.fitted)
+    Network::Network(const float maxT) : laplaceSmoothing(1), features(vector<string>()), className(""), classNumStates(0), maxThreads(maxT), fitted(false) {}
+    Network::Network(const float maxT, const int smoothing) : laplaceSmoothing(smoothing), features(vector<string>()), className(""), classNumStates(0), maxThreads(maxT), fitted(false) {}
+    Network::Network(const Network& other) : laplaceSmoothing(other.laplaceSmoothing), features(other.features), className(other.className), classNumStates(other.classNumStates), maxThreads(other.maxThreads), fitted(other.fitted)
     {
-        for (auto& pair : other.nodes) {
+        for (const auto& pair : other.nodes) {
             nodes[pair.first] = std::make_unique<Node>(*pair.second);
         }
     }
@@ -20,7 +20,7 @@ namespace bayesnet {
     {
         return samples;
     }
-    void Network::addNode(string name, int numStates)
+    void Network::addNode(const string& name, const int numStates)
     {
         if (find(features.begin(), features.end(), name) == features.end()) {
             features.push_back(name);
@@ -37,11 +37,11 @@ namespace bayesnet {
     {
         return features;
     }
-    int Network::getClassNumStates()
+    const int Network::getClassNumStates()
     {
         return classNumStates;
     }
-    int Network::getStates()
+    const int Network::getStates()
     {
         int result = 0;
         for (auto& node : nodes) {
@@ -49,7 +49,7 @@ namespace bayesnet {
         }
         return result;
     }
-    string Network::getClassName()
+    const string Network::getClassName()
     {
         return className;
     }
@@ -69,7 +69,7 @@ namespace bayesnet {
         recStack.erase(nodeId); // remove node from recursion stack before function ends
         return false;
     }
-    void Network::addEdge(const string parent, const string child)
+    void Network::addEdge(const string& parent, const string& child)
     {
         if (nodes.find(parent) == nodes.end()) {
             throw invalid_argument("Parent node " + parent + " does not exist");
@@ -105,8 +105,8 @@ namespace bayesnet {
         for (int i = 0; i < featureNames.size(); ++i) {
             auto column = torch::flatten(X.index({ "...", i }));
             auto k = vector<int>();
-            for (auto i = 0; i < X.size(0); ++i) {
-                k.push_back(column[i].item<int>());
+            for (auto z = 0; z < X.size(0); ++z) {
+                k.push_back(column[z].item<int>());
             }
             dataset[featureNames[i]] = k;
         }
@@ -262,9 +262,7 @@ namespace bayesnet {
 
         // Normalize result
         double sum = accumulate(result.begin(), result.end(), 0.0);
-        for (double& value : result) {
-            value /= sum;
-        }
+        transform(result.begin(), result.end(), result.begin(), [sum](double x) { return x / sum; });
         return result;
     }
     vector<string> Network::show()
@@ -280,7 +278,7 @@ namespace bayesnet {
         }
         return result;
     }
-    vector<string> Network::graph(string title)
+    vector<string> Network::graph(const string& title)
     {
         auto output = vector<string>();
         auto prefix = "digraph BayesNet {\nlabel=<BayesNet ";
diff --git a/src/BayesNet/Network.h b/src/BayesNet/Network.h
index c8d832d..690f6f2 100644
--- a/src/BayesNet/Network.h
+++ b/src/BayesNet/Network.h
@@ -27,19 +27,19 @@ namespace bayesnet {
         void completeFit();
     public:
         Network();
-        Network(float, int);
-        Network(float);
-        Network(Network&);
+        explicit Network(const float, const int);
+        explicit Network(const float);
+        explicit Network(const Network&);
         torch::Tensor& getSamples();
         float getmaxThreads();
-        void addNode(string, int);
-        void addEdge(const string, const string);
+        void addNode(const string&, const int);
+        void addEdge(const string&, const string&);
         map<string, std::unique_ptr<Node>>& getNodes();
         vector<string> getFeatures();
-        int getStates();
+        const int getStates();
         vector<pair<string, string>> getEdges();
-        int getClassNumStates();
-        string getClassName();
+        const int getClassNumStates();
+        const string getClassName();
         void fit(const vector<vector<int>>&, const vector<int>&, const vector<string>&, const string&);
         void fit(torch::Tensor&, torch::Tensor&, const vector<string>&, const string&);
         vector<int> predict(const vector<vector<int>>&);
@@ -48,7 +48,7 @@ namespace bayesnet {
         vector<vector<double>> predict_proba(const vector<vector<int>>&);
         double score(const vector<vector<int>>&, const vector<int>&);
         vector<string> show();
-        vector<string> graph(string title); // Returns a vector of strings representing the graph in graphviz format
+        vector<string> graph(const string& title); // Returns a vector of strings representing the graph in graphviz format
         inline string version() { return "0.1.0"; }
     };
 }
diff --git a/src/BayesNet/Node.cc b/src/BayesNet/Node.cc
index d33fecf..e25f807 100644
--- a/src/BayesNet/Node.cc
+++ b/src/BayesNet/Node.cc
@@ -88,18 +88,14 @@ namespace bayesnet {
     {
         // Get dimensions of the CPT
         dimensions.push_back(numStates);
-        for (auto father : getParents()) {
-            dimensions.push_back(father->getNumStates());
-        }
+        transform(parents.begin(), parents.end(), back_inserter(dimensions), [](Node* parent) { return parent->getNumStates(); });
         // Create a tensor of zeros with the dimensions of the CPT
         cpTable = torch::zeros(dimensions, torch::kFloat) + laplaceSmoothing;
         // Fill table with counts
         for (int n_sample = 0; n_sample < dataset[name].size(); ++n_sample) {
             torch::List<c10::optional<torch::Tensor>> coordinates;
             coordinates.push_back(torch::tensor(dataset[name][n_sample]));
-            for (auto father : getParents()) {
-                coordinates.push_back(torch::tensor(dataset[father->getName()][n_sample]));
-            }
+            transform(parents.begin(), parents.end(), back_inserter(coordinates), [&dataset, &n_sample](Node* parent) { return torch::tensor(dataset[parent->getName()][n_sample]); });
             // Increment the count of the corresponding coordinate
             cpTable.index_put_({ coordinates }, cpTable.index({ coordinates }) + 1);
         }
@@ -111,19 +107,15 @@ namespace bayesnet {
         torch::List<c10::optional<torch::Tensor>> coordinates;
         // following predetermined order of indices in the cpTable (see Node.h)
         coordinates.push_back(torch::tensor(evidence[name]));
-        for (auto parent : getParents()) {
-            coordinates.push_back(torch::tensor(evidence[parent->getName()]));
-        }
+        transform(parents.begin(), parents.end(), back_inserter(coordinates), [&evidence](Node* parent) { return torch::tensor(evidence[parent->getName()]); });
         return cpTable.index({ coordinates }).item<float>();
     }
-    vector<string> Node::graph(string className)
+    vector<string> Node::graph(const string& className)
     {
         auto output = vector<string>();
         auto suffix = name == className ? ", fontcolor=red, fillcolor=lightblue, style=filled " : "";
         output.push_back(name + " [shape=circle" + suffix + "] \n");
-        for (auto& child : children) {
-            output.push_back(name + " -> " + child->getName());
-        }
+        transform(children.begin(), children.end(), back_inserter(output), [this](Node* child) { return name + " -> " + child->getName() + "\n"; });
         return output;
     }
 }
\ No newline at end of file
diff --git a/src/BayesNet/Node.h b/src/BayesNet/Node.h
index 5c5932a..16c0386 100644
--- a/src/BayesNet/Node.h
+++ b/src/BayesNet/Node.h
@@ -30,7 +30,7 @@ namespace bayesnet {
         int getNumStates() const;
         void setNumStates(int);
         unsigned minFill();
-        vector<string> graph(string clasName); // Returns a vector of strings representing the graph in graphviz format
+        vector<string> graph(const string& clasName); // Returns a vector of strings representing the graph in graphviz format
         float getFactorValue(map<string, int>&);
     };
 }
diff --git a/src/BayesNet/SPODE.h b/src/BayesNet/SPODE.h
index 668bbca..ac1fd52 100644
--- a/src/BayesNet/SPODE.h
+++ b/src/BayesNet/SPODE.h
@@ -8,7 +8,7 @@ namespace bayesnet {
     protected:
         void train() override;
     public:
-        SPODE(int root);
+        explicit SPODE(int root);
         vector<string> graph(string name = "SPODE") override;
     };
 }
diff --git a/src/BayesNet/TAN.cc b/src/BayesNet/TAN.cc
index 9c8dfff..51f0c1b 100644
--- a/src/BayesNet/TAN.cc
+++ b/src/BayesNet/TAN.cc
@@ -18,7 +18,7 @@ namespace bayesnet {
             auto mi_value = metrics.mutualInformation(class_dataset, feature_dataset);
             mi.push_back({ i, mi_value });
         }
-        sort(mi.begin(), mi.end(), [](auto& left, auto& right) {return left.second < right.second;});
+        sort(mi.begin(), mi.end(), [](const auto& left, const auto& right) {return left.second < right.second;});
         auto root = mi[mi.size() - 1].first;
         // 2. Compute mutual information between each feature and the class
         auto weights = metrics.conditionalEdge();
diff --git a/src/Platform/CrossValidation.cc b/src/Platform/CrossValidation.cc
index b27788b..f19e19f 100644
--- a/src/Platform/CrossValidation.cc
+++ b/src/Platform/CrossValidation.cc
@@ -5,11 +5,11 @@ namespace platform {
     using json = nlohmann::json;
     using namespace std::chrono;
 
-    CrossValidation::CrossValidation(string modelName, bool stratified, int nfolds, vector<int> randomSeeds, platform::Datasets& datasets) : modelName(modelName), stratified(stratified), nfolds(nfolds), randomSeeds(randomSeeds), datasets(datasets)
+    CrossValidation::CrossValidation(const string& modelName, bool stratified, int nfolds, const vector<int>& randomSeeds, platform::Datasets& datasets) : modelName(modelName), stratified(stratified), nfolds(nfolds), randomSeeds(randomSeeds), datasets(datasets)
     {
     }
 
-    Result CrossValidation::crossValidate(string fileName)
+    Result CrossValidation::crossValidate(const string& fileName)
     {
         auto [Xt, y] = datasets.getTensors(fileName);
         auto states = datasets.getStates(fileName);
diff --git a/src/Platform/CrossValidation.h b/src/Platform/CrossValidation.h
index ff084d7..43a19db 100644
--- a/src/Platform/CrossValidation.h
+++ b/src/Platform/CrossValidation.h
@@ -17,9 +17,9 @@ namespace platform {
         vector<int> randomSeeds;
         platform::Datasets& datasets;
     public:
-        CrossValidation(string modelName, bool stratified, int nfolds, vector<int> randomSeeds, platform::Datasets& datasets);
+        CrossValidation(const string& modelName, bool stratified, int nfolds, const vector<int>& randomSeeds, platform::Datasets& datasets);
         ~CrossValidation() = default;
-        Result crossValidate(string fileName);
+        Result crossValidate(const string& fileName);
     };
 }
 #endif // !CROSSVALIDATION_H
\ No newline at end of file
diff --git a/src/Platform/Datasets.cc b/src/Platform/Datasets.cc
index 0c09c59..1287533 100644
--- a/src/Platform/Datasets.cc
+++ b/src/Platform/Datasets.cc
@@ -2,7 +2,7 @@
 #include "platformUtils.h"
 #include "ArffFiles.h"
 namespace platform {
-    vector<string> split(string text, char delimiter)
+    vector<string> split(const string& text, char delimiter)
     {
         vector<string> result;
         stringstream ss(text);
@@ -14,9 +14,9 @@ namespace platform {
     }
     void Datasets::load()
     {
-        string line;
         ifstream catalog(path + "/all.txt");
         if (catalog.is_open()) {
+            string line;
             while (getline(catalog, line)) {
                 vector<string> tokens = split(line, ',');
                 string name = tokens[0];
@@ -31,12 +31,10 @@ namespace platform {
     vector<string> Datasets::getNames()
     {
         vector<string> result;
-        for (auto& d : datasets) {
-            result.push_back(d.first);
-        }
+        transform(datasets.begin(), datasets.end(), back_inserter(result), [](const auto& d) { return d.first; });
         return result;
     }
-    vector<string> Datasets::getFeatures(string name)
+    vector<string> Datasets::getFeatures(const string& name)
     {
         if (datasets[name]->isLoaded()) {
             return datasets[name]->getFeatures();
@@ -44,7 +42,7 @@ namespace platform {
             throw invalid_argument("Dataset not loaded.");
         }
     }
-    map<string, vector<int>> Datasets::getStates(string name)
+    map<string, vector<int>> Datasets::getStates(const string& name)
     {
         if (datasets[name]->isLoaded()) {
             return datasets[name]->getStates();
@@ -52,7 +50,7 @@ namespace platform {
             throw invalid_argument("Dataset not loaded.");
         }
     }
-    string Datasets::getClassName(string name)
+    string Datasets::getClassName(const string& name)
     {
         if (datasets[name]->isLoaded()) {
             return datasets[name]->getClassName();
@@ -60,7 +58,7 @@ namespace platform {
             throw invalid_argument("Dataset not loaded.");
         }
     }
-    int Datasets::getNSamples(string name)
+    int Datasets::getNSamples(const string& name)
     {
         if (datasets[name]->isLoaded()) {
             return datasets[name]->getNSamples();
@@ -68,49 +66,32 @@ namespace platform {
             throw invalid_argument("Dataset not loaded.");
         }
     }
-    pair<vector<vector<float>>&, vector<int>&> Datasets::getVectors(string name)
+    pair<vector<vector<float>>&, vector<int>&> Datasets::getVectors(const string& name)
     {
         if (!datasets[name]->isLoaded()) {
             datasets[name]->load();
         }
         return datasets[name]->getVectors();
     }
-    pair<vector<vector<int>>&, vector<int>&> Datasets::getVectorsDiscretized(string name)
+    pair<vector<vector<int>>&, vector<int>&> Datasets::getVectorsDiscretized(const string& name)
     {
         if (!datasets[name]->isLoaded()) {
             datasets[name]->load();
         }
         return datasets[name]->getVectorsDiscretized();
     }
-    pair<torch::Tensor&, torch::Tensor&> Datasets::getTensors(string name)
+    pair<torch::Tensor&, torch::Tensor&> Datasets::getTensors(const string& name)
     {
         if (!datasets[name]->isLoaded()) {
             datasets[name]->load();
         }
         return datasets[name]->getTensors();
     }
-    bool Datasets::isDataset(string name)
+    bool Datasets::isDataset(const string& name)
     {
         return datasets.find(name) != datasets.end();
     }
-    Dataset::Dataset(Dataset& dataset)
-    {
-        path = dataset.path;
-        name = dataset.name;
-        className = dataset.className;
-        n_samples = dataset.n_samples;
-        n_features = dataset.n_features;
-        features = dataset.features;
-        states = dataset.states;
-        loaded = dataset.loaded;
-        discretize = dataset.discretize;
-        X = dataset.X;
-        y = dataset.y;
-        Xv = dataset.Xv;
-        Xd = dataset.Xd;
-        yv = dataset.yv;
-        fileType = dataset.fileType;
-    }
+    Dataset::Dataset(Dataset& dataset) : path(dataset.path), name(dataset.name), className(dataset.className), n_samples(dataset.n_samples), n_features(dataset.n_features), features(dataset.features), states(dataset.states), loaded(dataset.loaded), discretize(dataset.discretize), X(dataset.X), y(dataset.y), Xv(dataset.Xv), Xd(dataset.Xd), yv(dataset.yv), fileType(dataset.fileType) {}
     string Dataset::getName()
     {
         return name;
@@ -178,9 +159,9 @@ namespace platform {
     }
     void Dataset::load_csv()
     {
-        string line;
         ifstream file(path + "/" + name + ".csv");
         if (file.is_open()) {
+            string line;
             getline(file, line);
             vector<string> tokens = split(line, ',');
             features = vector<string>(tokens.begin(), tokens.end() - 1);
@@ -218,9 +199,8 @@ namespace platform {
         yv = arff.getY();
         // Get className & Features
         className = arff.getClassName();
-        for (auto feature : arff.getAttributes()) {
-            features.push_back(feature.first);
-        }
+        auto attributes = arff.getAttributes();
+        transform(attributes.begin(), attributes.end(), back_inserter(features), [](const auto& f) { return f.first; });
     }
     void Dataset::load()
     {
diff --git a/src/Platform/Datasets.h b/src/Platform/Datasets.h
index 1d178b1..57c9c5f 100644
--- a/src/Platform/Datasets.h
+++ b/src/Platform/Datasets.h
@@ -27,8 +27,8 @@ namespace platform {
         void load_arff();
         void computeStates();
     public:
-        Dataset(string path, string name, string className, bool discretize, fileType_t fileType) : path(path), name(name), className(className), discretize(discretize), loaded(false), fileType(fileType) {};
-        Dataset(Dataset&);
+        Dataset(const string& path, const string& name, const string& className, bool discretize, fileType_t fileType) : path(path), name(name), className(className), discretize(discretize), loaded(false), fileType(fileType), n_samples(0), n_features(0) {};
+        explicit Dataset(Dataset&);
         string getName();
         string getClassName();
         vector<string> getFeatures();
@@ -49,18 +49,18 @@ namespace platform {
         bool discretize;
         void load(); // Loads the list of datasets
     public:
-        Datasets(string path, bool discretize = false, fileType_t fileType = ARFF) : path(path), discretize(discretize), fileType(fileType) { load(); };
+        explicit Datasets(const string& path, bool discretize = false, fileType_t fileType = ARFF) : path(path), discretize(discretize), fileType(fileType) { load(); };
         vector<string> getNames();
-        vector<string> getFeatures(string name);
-        int getNSamples(string name);
-        string getClassName(string name);
-        map<string, vector<int>> getStates(string name);
-        pair<vector<vector<float>>&, vector<int>&> getVectors(string name);
-        pair<vector<vector<int>>&, vector<int>&> getVectorsDiscretized(string name);
-        pair<torch::Tensor&, torch::Tensor&> getTensors(string name);
-        bool isDataset(string name);
+        vector<string> getFeatures(const string& name);
+        int getNSamples(const string& name);
+        string getClassName(const string& name);
+        map<string, vector<int>> getStates(const string& name);
+        pair<vector<vector<float>>&, vector<int>&> getVectors(const string& name);
+        pair<vector<vector<int>>&, vector<int>&> getVectorsDiscretized(const string& name);
+        pair<torch::Tensor&, torch::Tensor&> getTensors(const string& name);
+        bool isDataset(const string& name);
     };
-    vector<string> split(string, char);
+    vector<string> split(const string&, char);
 };
 
 #endif
\ No newline at end of file
diff --git a/src/Platform/DotEnv.h b/src/Platform/DotEnv.h
index 4c3783f..189a79e 100644
--- a/src/Platform/DotEnv.h
+++ b/src/Platform/DotEnv.h
@@ -60,9 +60,7 @@ public:
         seeds_str = trim(seeds_str);
         seeds_str = seeds_str.substr(1, seeds_str.size() - 2);
         auto seeds_str_split = split(seeds_str, ',');
-        for (auto seed_str : seeds_str_split) {
-            seeds.push_back(stoi(seed_str));
-        }
+        transform(seeds_str_split.begin(), seeds_str_split.end(), back_inserter(seeds), [](const auto& s) { return stoi(s); });
         return seeds;
     }
 };
diff --git a/src/Platform/Experiment.cc b/src/Platform/Experiment.cc
index b86fa03..2846e08 100644
--- a/src/Platform/Experiment.cc
+++ b/src/Platform/Experiment.cc
@@ -46,7 +46,7 @@ namespace platform {
         result["seeds"] = random_seeds;
         result["duration"] = duration;
         result["results"] = json::array();
-        for (auto& r : results) {
+        for (const auto& r : results) {
             json j;
             j["dataset"] = r.getDataset();
             j["hyperparameters"] = r.getHyperparameters();
@@ -76,7 +76,7 @@ namespace platform {
         }
         return result;
     }
-    void Experiment::save(string path)
+    void Experiment::save(const string& path)
     {
         json data = build_json();
         ofstream file(path + "/" + get_file_name());
diff --git a/src/Platform/Experiment.h b/src/Platform/Experiment.h
index a9cadfb..9d1e5d2 100644
--- a/src/Platform/Experiment.h
+++ b/src/Platform/Experiment.h
@@ -24,34 +24,34 @@ namespace platform {
     };
     class Result {
     private:
-        string dataset, hyperparameters, model_version;
-        int samples, features, classes;
-        double score_train, score_test, score_train_std, score_test_std, train_time, train_time_std, test_time, test_time_std;
-        vector<double> scores_train, scores_test, times_train, times_test;
-        float nodes, leaves, depth;
+        string dataset = "", hyperparameters = "", model_version = "";
+        int samples{ 0 }, features{ 0 }, classes{ 0 };
+        double score_train{ 0 }, score_test = 0, score_train_std = 0, score_test_std = 0, train_time = 0, train_time_std = 0, test_time = 0, test_time_std = 0;
+        vector<double> scores_train{}, scores_test{}, times_train{}, times_test{};
+        float nodes{ 0 }, leaves{ 0 }, depth{ 0 };
     public:
         Result() = default;
-        Result& setDataset(string dataset) { this->dataset = dataset; return *this; }
-        Result& setHyperparameters(string hyperparameters) { this->hyperparameters = hyperparameters; return *this; }
-        Result& setSamples(int samples) { this->samples = samples; return *this; }
-        Result& setFeatures(int features) { this->features = features; return *this; }
-        Result& setClasses(int classes) { this->classes = classes; return *this; }
-        Result& setScoreTrain(double score) { this->score_train = score; return *this; }
-        Result& setScoreTest(double score) { this->score_test = score; return *this; }
-        Result& setScoreTrainStd(double score_std) { this->score_train_std = score_std; return *this; }
-        Result& setScoreTestStd(double score_std) { this->score_test_std = score_std; return *this; }
-        Result& setTrainTime(double train_time) { this->train_time = train_time; return *this; }
-        Result& setTrainTimeStd(double train_time_std) { this->train_time_std = train_time_std; return *this; }
-        Result& setTestTime(double test_time) { this->test_time = test_time; return *this; }
-        Result& setTestTimeStd(double test_time_std) { this->test_time_std = test_time_std; return *this; }
-        Result& setNodes(float nodes) { this->nodes = nodes; return *this; }
-        Result& setLeaves(float leaves) { this->leaves = leaves; return *this; }
-        Result& setDepth(float depth) { this->depth = depth; return *this; }
-        Result& setModelVersion(string model_version) { this->model_version = model_version; return *this; }
-        Result& addScoreTrain(double score) { scores_train.push_back(score); return *this; }
-        Result& addScoreTest(double score) { scores_test.push_back(score); return *this; }
-        Result& addTimeTrain(double time) { times_train.push_back(time); return *this; }
-        Result& addTimeTest(double time) { times_test.push_back(time); return *this; }
+        Result& setDataset(const string& dataset) { this->dataset = dataset; return *this; }
+        Result& setHyperparameters(const string& hyperparameters) { this->hyperparameters = hyperparameters; return *this; }
+        Result& setSamples(const int samples) { this->samples = samples; return *this; }
+        Result& setFeatures(const int features) { this->features = features; return *this; }
+        Result& setClasses(const int classes) { this->classes = classes; return *this; }
+        Result& setScoreTrain(const double score) { this->score_train = score; return *this; }
+        Result& setScoreTest(const double score) { this->score_test = score; return *this; }
+        Result& setScoreTrainStd(const double score_std) { this->score_train_std = score_std; return *this; }
+        Result& setScoreTestStd(const double score_std) { this->score_test_std = score_std; return *this; }
+        Result& setTrainTime(const double train_time) { this->train_time = train_time; return *this; }
+        Result& setTrainTimeStd(const double train_time_std) { this->train_time_std = train_time_std; return *this; }
+        Result& setTestTime(const double test_time) { this->test_time = test_time; return *this; }
+        Result& setTestTimeStd(const double test_time_std) { this->test_time_std = test_time_std; return *this; }
+        Result& setNodes(const float nodes) { this->nodes = nodes; return *this; }
+        Result& setLeaves(const float leaves) { this->leaves = leaves; return *this; }
+        Result& setDepth(const float depth) { this->depth = depth; return *this; }
+        Result& setModelVersion(const string& model_version) { this->model_version = model_version; return *this; }
+        Result& addScoreTrain(const double score) { scores_train.push_back(score); return *this; }
+        Result& addScoreTest(const double score) { scores_test.push_back(score); return *this; }
+        Result& addTimeTrain(const double time) { times_train.push_back(time); return *this; }
+        Result& addTimeTest(const double time) { times_test.push_back(time); return *this; }
         const float get_score_train() const { return score_train; }
         float get_score_test() { return score_test; }
         const string& getDataset() const { return dataset; }
@@ -78,30 +78,30 @@ namespace platform {
     };
     class Experiment {
     private:
-        string title, model, platform, score_name, model_version, language_version, language;
-        bool discretized, stratified;
+        string title{""}, model{""}, platform{""}, score_name{""}, model_version{""}, language_version{""}, language{""};
+        bool discretized{false}, stratified{false};
         vector<Result> results;
         vector<int> random_seeds;
-        int nfolds;
-        float duration;
+        int nfolds{0};
+        float duration{0};
         json build_json();
     public:
         Experiment() = default;
-        Experiment& setTitle(string title) { this->title = title; return *this; }
-        Experiment& setModel(string model) { this->model = model; return *this; }
-        Experiment& setPlatform(string platform) { this->platform = platform; return *this; }
-        Experiment& setScoreName(string score_name) { this->score_name = score_name; return *this; }
-        Experiment& setModelVersion(string model_version) { this->model_version = model_version; return *this; }
-        Experiment& setLanguage(string language) { this->language = language; return *this; }
-        Experiment& setLanguageVersion(string language_version) { this->language_version = language_version; return *this; }
-        Experiment& setDiscretized(bool discretized) { this->discretized = discretized; return *this; }
-        Experiment& setStratified(bool stratified) { this->stratified = stratified; return *this; }
-        Experiment& setNFolds(int nfolds) { this->nfolds = nfolds; return *this; }
+        Experiment& setTitle(const string& title) { this->title = title; return *this; }
+        Experiment& setModel(const string& model) { this->model = model; return *this; }
+        Experiment& setPlatform(const string& platform) { this->platform = platform; return *this; }
+        Experiment& setScoreName(const string& score_name) { this->score_name = score_name; return *this; }
+        Experiment& setModelVersion(const string& model_version) { this->model_version = model_version; return *this; }
+        Experiment& setLanguage(const string& language) { this->language = language; return *this; }
+        Experiment& setLanguageVersion(const string& language_version) { this->language_version = language_version; return *this; }
+        Experiment& setDiscretized(const bool discretized) { this->discretized = discretized; return *this; }
+        Experiment& setStratified(const bool stratified) { this->stratified = stratified; return *this; }
+        Experiment& setNFolds(const int nfolds) { this->nfolds = nfolds; return *this; }
         Experiment& addResult(Result result) { results.push_back(result); return *this; }
-        Experiment& addRandomSeed(int random_seed) { random_seeds.push_back(random_seed); return *this; }
-        Experiment& setDuration(float duration) { this->duration = duration; return *this; }
+        Experiment& addRandomSeed(const int random_seed) { random_seeds.push_back(random_seed); return *this; }
+        Experiment& setDuration(const float duration) { this->duration = duration; return *this; }
         string get_file_name();
-        void save(string path);
+        void save(const string& path);
         void show();
     };
 }
diff --git a/src/Platform/Folding.cc b/src/Platform/Folding.cc
index ec7c4b5..1a8091e 100644
--- a/src/Platform/Folding.cc
+++ b/src/Platform/Folding.cc
@@ -7,9 +7,8 @@ Fold::Fold(int k, int n, int seed) : k(k), n(n), seed(seed)
     random_seed = default_random_engine(seed == -1 ? rd() : seed);
     srand(seed == -1 ? time(0) : seed);
 }
-KFold::KFold(int k, int n, int seed) : Fold(k, n, seed)
+KFold::KFold(int k, int n, int seed) : Fold(k, n, seed), indices(vector<int>())
 {
-    indices = vector<int>(n);
     iota(begin(indices), end(indices), 0); // fill with 0, 1, ..., n - 1
     shuffle(indices.begin(), indices.end(), random_seed);
 }
diff --git a/src/Platform/Folding.h b/src/Platform/Folding.h
index d7736d0..eaf0c4b 100644
--- a/src/Platform/Folding.h
+++ b/src/Platform/Folding.h
@@ -22,7 +22,7 @@ private:
     vector<int> indices;
 public:
     KFold(int k, int n, int seed = -1);
-    pair<vector<int>, vector<int>> getFold(int nFold);
+    pair<vector<int>, vector<int>> getFold(int nFold) override;
 };
 class StratifiedKFold : public Fold {
 private:
@@ -32,6 +32,6 @@ private:
 public:
     StratifiedKFold(int k, const vector<int>& y, int seed = -1);
     StratifiedKFold(int k, torch::Tensor& y, int seed = -1);
-    pair<vector<int>, vector<int>> getFold(int nFold);
+    pair<vector<int>, vector<int>> getFold(int nFold) override;
 };
 #endif
\ No newline at end of file
diff --git a/src/Platform/main.cc b/src/Platform/main.cc
index 25984d6..381c168 100644
--- a/src/Platform/main.cc
+++ b/src/Platform/main.cc
@@ -50,22 +50,17 @@ argparse::ArgumentParser manageArguments(int argc, char** argv)
         }});
     auto seed_values = env.getSeeds();
     program.add_argument("-s", "--seeds").nargs(1, 10).help("Random seeds. Set to -1 to have pseudo random").scan<'i', int>().default_value(seed_values);
-    bool class_last, discretize_dataset, stratified;
-    int n_folds;
-    vector<int> seeds;
-    string model_name, file_name, path, complete_file_name, title;
     try {
         program.parse_args(argc, argv);
-        file_name = program.get<string>("dataset");
-        path = program.get<string>("path");
-        model_name = program.get<string>("model");
-        discretize_dataset = program.get<bool>("discretize");
-        stratified = program.get<bool>("stratified");
-        n_folds = program.get<int>("folds");
-        seeds = program.get<vector<int>>("seeds");
-        complete_file_name = path + file_name + ".arff";
-        class_last = false;//datasets[file_name];
-        title = program.get<string>("title");
+        auto file_name = program.get<string>("dataset");
+        auto path = program.get<string>("path");
+        auto model_name = program.get<string>("model");
+        auto discretize_dataset = program.get<bool>("discretize");
+        auto stratified = program.get<bool>("stratified");
+        auto n_folds = program.get<int>("folds");
+        auto seeds = program.get<vector<int>>("seeds");
+        auto complete_file_name = path + file_name + ".arff";
+        auto title = program.get<string>("title");
         if (title == "" && file_name == "") {
             throw runtime_error("title is mandatory if dataset is not provided");
         }
@@ -81,7 +76,6 @@ argparse::ArgumentParser manageArguments(int argc, char** argv)
 int main(int argc, char** argv)
 {
     auto program = manageArguments(argc, argv);
-    auto env = DotEnv();
     bool saveResults = false;
     auto file_name = program.get<string>("dataset");
     auto path = program.get<string>("path");
@@ -124,10 +118,8 @@ int main(int argc, char** argv)
     for (auto fileName : filesToProcess) {
         cout << "- " << setw(20) << left << fileName << " " << right << flush;
         auto [X, y] = datasets.getTensors(fileName);
-        auto states = datasets.getStates(fileName);
         auto features = datasets.getFeatures(fileName);
         auto samples = datasets.getNSamples(fileName);
-        auto className = datasets.getClassName(fileName);
         cout << " (" << setw(5) << samples << "," << setw(3) << features.size() << ") " << flush;
         auto result = validation.crossValidate(fileName);
         result.setDataset(fileName);
diff --git a/src/Platform/platformUtils.cc b/src/Platform/platformUtils.cc
index ea8fad3..d999353 100644
--- a/src/Platform/platformUtils.cc
+++ b/src/Platform/platformUtils.cc
@@ -38,7 +38,7 @@ bool file_exists(const std::string& name)
     }
 }
 
-tuple<Tensor, Tensor, vector<string>, string, map<string, vector<int>>> loadDataset(string path, string name, bool class_last, bool discretize_dataset)
+tuple<Tensor, Tensor, vector<string>, string, map<string, vector<int>>> loadDataset(const string& path, const string& name, bool class_last, bool discretize_dataset)
 {
     auto handler = ArffFiles();
     handler.load(path + static_cast<string>(name) + ".arff", class_last);
@@ -48,9 +48,8 @@ tuple<Tensor, Tensor, vector<string>, string, map<string, vector<int>>> loadData
     // Get className & Features
     auto className = handler.getClassName();
     vector<string> features;
-    for (auto feature : handler.getAttributes()) {
-        features.push_back(feature.first);
-    }
+    auto attributes = handler.getAttributes();
+    transform(attributes.begin(), attributes.end(), back_inserter(features), [](const auto& f) { return f.first; });
     Tensor Xd;
     auto states = map<string, vector<int>>();
     if (discretize_dataset) {
@@ -72,7 +71,7 @@ tuple<Tensor, Tensor, vector<string>, string, map<string, vector<int>>> loadData
     return { Xd, torch::tensor(y, torch::kInt32), features, className, states };
 }
 
-tuple<vector<vector<int>>, vector<int>, vector<string>, string, map<string, vector<int>>> loadFile(string name)
+tuple<vector<vector<int>>, vector<int>, vector<string>, string, map<string, vector<int>>> loadFile(const string& name)
 {
     auto handler = ArffFiles();
     handler.load(PATH + static_cast<string>(name) + ".arff");
@@ -82,9 +81,8 @@ tuple<vector<vector<int>>, vector<int>, vector<string>, string, map<string, vect
     // Get className & Features
     auto className = handler.getClassName();
     vector<string> features;
-    for (auto feature : handler.getAttributes()) {
-        features.push_back(feature.first);
-    }
+    auto attributes = handler.getAttributes();
+    transform(attributes.begin(), attributes.end(), back_inserter(features), [](const auto& f) { return f.first; });
     // Discretize Dataset
     vector<mdlp::labels_t> Xd;
     map<string, int> maxes;
diff --git a/src/Platform/platformUtils.h b/src/Platform/platformUtils.h
index abc69bd..8d9a75f 100644
--- a/src/Platform/platformUtils.h
+++ b/src/Platform/platformUtils.h
@@ -13,8 +13,8 @@ const string PATH = "../../data/";
 bool file_exists(const std::string& name);
 pair<vector<mdlp::labels_t>, map<string, int>> discretize(vector<mdlp::samples_t>& X, mdlp::labels_t& y, vector<string> features);
 vector<mdlp::labels_t> discretizeDataset(vector<mdlp::samples_t>& X, mdlp::labels_t& y);
-pair<torch::Tensor, map<string, vector<int>>> discretizeTorch(torch::Tensor& X, torch::Tensor& y, vector<string>& features, string className);
-tuple<vector<vector<int>>, vector<int>, vector<string>, string, map<string, vector<int>>> loadFile(string name);
-tuple<torch::Tensor, torch::Tensor, vector<string>, string, map<string, vector<int>>> loadDataset(string path, string name, bool class_last, bool discretize_dataset);
+// pair<torch::Tensor, map<string, vector<int>>> discretizeTorch(torch::Tensor& X, torch::Tensor& y, vector<string>& features, const string& className);
+tuple<vector<vector<int>>, vector<int>, vector<string>, string, map<string, vector<int>>> loadFile(const string& name);
+tuple<torch::Tensor, torch::Tensor, vector<string>, string, map<string, vector<int>>> loadDataset(const string& path, const string& name, bool class_last, bool discretize_dataset);
 map<string, vector<int>> get_states(vector<string>& features, string className, map<string, int>& maxes);
 #endif //PLATFORM_UTILS_H