diff --git a/bayesnet/BaseClassifier.h b/bayesnet/BaseClassifier.h
index 69f4b29..c237349 100644
--- a/bayesnet/BaseClassifier.h
+++ b/bayesnet/BaseClassifier.h
@@ -30,7 +30,7 @@ namespace bayesnet {
         virtual std::string getVersion() = 0;
         std::vector<std::string> virtual topological_order() = 0;
         std::vector<std::string> virtual getNotes() const = 0;
-        void virtual dump_cpt()const = 0;
+        std::string virtual dump_cpt()const = 0;
         virtual void setHyperparameters(const nlohmann::json& hyperparameters) = 0;
         std::vector<std::string>& getValidHyperparameters() { return validHyperparameters; }
     protected:
diff --git a/bayesnet/classifiers/Classifier.cc b/bayesnet/classifiers/Classifier.cc
index 19f337a..a8bf6ef 100644
--- a/bayesnet/classifiers/Classifier.cc
+++ b/bayesnet/classifiers/Classifier.cc
@@ -75,11 +75,11 @@ namespace bayesnet {
         if (torch::is_floating_point(dataset)) {
             throw std::invalid_argument("dataset (X, y) must be of type Integer");
         }
-        if (n != features.size()) {
-            throw std::invalid_argument("Classifier: X " + std::to_string(n) + " and features " + std::to_string(features.size()) + " must have the same number of features");
+        if (dataset.size(0) - 1 != features.size()) {
+            throw std::invalid_argument("Classifier: X " + std::to_string(dataset.size(0) - 1) + " and features " + std::to_string(features.size()) + " must have the same number of features");
         }
         if (states.find(className) == states.end()) {
-            throw std::invalid_argument("className not found in states");
+            throw std::invalid_argument("class name not found in states");
         }
         for (auto feature : features) {
             if (states.find(feature) == states.end()) {
@@ -175,9 +175,9 @@ namespace bayesnet {
     {
         return model.topological_sort();
     }
-    void Classifier::dump_cpt() const
+    std::string Classifier::dump_cpt() const
     {
-        model.dump_cpt();
+        return model.dump_cpt();
     }
     void Classifier::setHyperparameters(const nlohmann::json& hyperparameters)
     {
diff --git a/bayesnet/classifiers/Classifier.h b/bayesnet/classifiers/Classifier.h
index c7685a2..2511c4d 100644
--- a/bayesnet/classifiers/Classifier.h
+++ b/bayesnet/classifiers/Classifier.h
@@ -30,7 +30,7 @@ namespace bayesnet {
         std::vector<std::string> show() const override;
         std::vector<std::string> topological_order()  override;
         std::vector<std::string> getNotes() const override { return notes; }
-        void dump_cpt() const override;
+        std::string dump_cpt() const override;
         void setHyperparameters(const nlohmann::json& hyperparameters) override; //For classifiers that don't have hyperparameters
     protected:
         bool fitted;
diff --git a/bayesnet/ensembles/Ensemble.h b/bayesnet/ensembles/Ensemble.h
index cb4220a..bab4d25 100644
--- a/bayesnet/ensembles/Ensemble.h
+++ b/bayesnet/ensembles/Ensemble.h
@@ -25,8 +25,9 @@ namespace bayesnet {
         {
             return std::vector<std::string>();
         }
-        void dump_cpt() const override
+        std::string dump_cpt() const override
         {
+            return "";
         }
     protected:
         torch::Tensor predict_average_voting(torch::Tensor& X);
diff --git a/tests/TestBayesClassifier.cc b/tests/TestBayesClassifier.cc
index d07adbd..1e33e67 100644
--- a/tests/TestBayesClassifier.cc
+++ b/tests/TestBayesClassifier.cc
@@ -20,4 +20,67 @@ TEST_CASE("Test Cannot build dataset with wrong data tensor", "[Classifier]")
     auto yshort = torch::zeros({ 149 }, torch::kInt32);
     REQUIRE_THROWS_AS(model.fit(raw.Xt, yshort, raw.featurest, raw.classNamet, raw.statest), std::runtime_error);
     REQUIRE_THROWS_WITH(model.fit(raw.Xt, yshort, raw.featurest, raw.classNamet, raw.statest), "* Error in X and y dimensions *\nX dimensions: [4, 150]\ny dimensions: [149]");
+}
+TEST_CASE("Invalid data type", "[Classifier]")
+{
+    auto model = bayesnet::TAN();
+    auto raw = RawDatasets("iris", false);
+    REQUIRE_THROWS_AS(model.fit(raw.Xt, raw.yt, raw.featurest, raw.classNamet, raw.statest), std::invalid_argument);
+    REQUIRE_THROWS_WITH(model.fit(raw.Xt, raw.yt, raw.featurest, raw.classNamet, raw.statest), "dataset (X, y) must be of type Integer");
+}
+TEST_CASE("Invalid number of features", "[Classifier]")
+{
+    auto model = bayesnet::TAN();
+    auto raw = RawDatasets("iris", true);
+    auto Xt = torch::cat({ raw.Xt, torch::zeros({ 1, 150 }, torch::kInt32) }, 0);
+    REQUIRE_THROWS_AS(model.fit(Xt, raw.yt, raw.featurest, raw.classNamet, raw.statest), std::invalid_argument);
+    REQUIRE_THROWS_WITH(model.fit(Xt, raw.yt, raw.featurest, raw.classNamet, raw.statest), "Classifier: X 5 and features 4 must have the same number of features");
+}
+TEST_CASE("Invalid class name", "[Classifier]")
+{
+    auto model = bayesnet::TAN();
+    auto raw = RawDatasets("iris", true);
+    REQUIRE_THROWS_AS(model.fit(raw.Xt, raw.yt, raw.featurest, "duck", raw.statest), std::invalid_argument);
+    REQUIRE_THROWS_WITH(model.fit(raw.Xt, raw.yt, raw.featurest, "duck", raw.statest), "class name not found in states");
+}
+TEST_CASE("Invalid feature name", "[Classifier]")
+{
+    auto model = bayesnet::TAN();
+    auto raw = RawDatasets("iris", true);
+    auto statest = raw.statest;
+    statest.erase("petallength");
+    REQUIRE_THROWS_AS(model.fit(raw.Xt, raw.yt, raw.featurest, raw.classNamet, statest), std::invalid_argument);
+    REQUIRE_THROWS_WITH(model.fit(raw.Xt, raw.yt, raw.featurest, raw.classNamet, statest), "feature [petallength] not found in states");
+}
+TEST_CASE("Topological order", "[Classifier]")
+{
+    auto model = bayesnet::TAN();
+    auto raw = RawDatasets("iris", true);
+    model.fit(raw.Xt, raw.yt, raw.featurest, raw.classNamet, raw.statest);
+    auto order = model.topological_order();
+    REQUIRE(order.size() == 4);
+    REQUIRE(order[0] == "petallength");
+    REQUIRE(order[1] == "sepallength");
+    REQUIRE(order[2] == "sepalwidth");
+    REQUIRE(order[3] == "petalwidth");
+}
+TEST_CASE("Not fitted model", "[Classifier]")
+{
+    auto model = bayesnet::TAN();
+    auto raw = RawDatasets("iris", true);
+    auto message = "Classifier has not been fitted";
+    // tensors
+    REQUIRE_THROWS_AS(model.predict(raw.Xt), std::logic_error);
+    REQUIRE_THROWS_WITH(model.predict(raw.Xt), message);
+    REQUIRE_THROWS_AS(model.predict_proba(raw.Xt), std::logic_error);
+    REQUIRE_THROWS_WITH(model.predict_proba(raw.Xt), message);
+    REQUIRE_THROWS_AS(model.score(raw.Xt, raw.yt), std::logic_error);
+    REQUIRE_THROWS_WITH(model.score(raw.Xt, raw.yt), message);
+    // vectors
+    REQUIRE_THROWS_AS(model.predict(raw.Xv), std::logic_error);
+    REQUIRE_THROWS_WITH(model.predict(raw.Xv), message);
+    REQUIRE_THROWS_AS(model.predict_proba(raw.Xv), std::logic_error);
+    REQUIRE_THROWS_WITH(model.predict_proba(raw.Xv), message);
+    REQUIRE_THROWS_AS(model.score(raw.Xv, raw.yv), std::logic_error);
+    REQUIRE_THROWS_WITH(model.score(raw.Xv, raw.yv), message);
 }
\ No newline at end of file
diff --git a/tests/TestBayesModels.cc b/tests/TestBayesModels.cc
index bfa7169..51d5091 100644
--- a/tests/TestBayesModels.cc
+++ b/tests/TestBayesModels.cc
@@ -246,7 +246,7 @@ TEST_CASE("BoostAODE voting-proba", "[Models]")
     REQUIRE(score_voting == Catch::Approx(0.98).epsilon(raw.epsilon));
     REQUIRE(pred_voting[83][2] == Catch::Approx(0.552091).epsilon(raw.epsilon));
     REQUIRE(pred_proba[83][2] == Catch::Approx(0.546017).epsilon(raw.epsilon));
-    clf.dump_cpt();
+    REQUIRE(clf.dump_cpt() == "");
     REQUIRE(clf.topological_order() == std::vector<std::string>());
 }
 TEST_CASE("AODE voting-proba", "[Models]")