training a data set on a Decision tree cls

Decision_Trees/moons_dataset.py

@@ -0,0 +1,58 @@
+#%%
+from sklearn.datasets import make_moons
+
+X,Y = make_moons(n_samples=10000,noise=0.4,random_state=42)
+print(X)
+print(Y)
+
+#%%
+from sklearn.model_selection import train_test_split
+
+X_train,X_test,Y_train,Y_test= train_test_split(
+    X,Y,test_size=0.2,random_state=42)
+print(X_train)
+print(Y_train)
+
+#%%
+def display_scores(scores):
+    print("Scores:", scores)
+    print("Mean:", scores.mean())
+    print("Standard deviation:", scores.std())
+
+from sklearn.model_selection import GridSearchCV
+from sklearn.model_selection import cross_val_score
+from sklearn.tree import DecisionTreeClassifier
+
+
+param_grid = [
+    {'max_depth':[2,5,10,20],
+    'max_leaf_nodes':[5,10,15,20,50],
+    'min_impurity_split':[0.2,0.3,0.4]}
+]
+decision_tree_cls = DecisionTreeClassifier()
+_search = GridSearchCV(decision_tree_cls, param_grid, cv=10,
+scoring='neg_mean_squared_error')
+_search.fit(X_train, Y_train)
+_search.best_params_
+
+#%%
+# from sklearn.model_selection import RandomizedSearchCV
+# _search = RandomizedSearchCV(decision_tree_cls, 
+#             param_distributions=param_grid,
+#             n_iter=50, cv=5, scoring='neg_mean_squared_error',
+#             verbose=2, n_jobs=4, random_state=42)
+# _search.fit(X_train, Y_train)
+# _search.best_params_
+
+
+#%%
+from sklearn.metrics import mean_squared_error
+import numpy as np
+final_model = _search.best_estimator_
+prediction = final_model.predict(X_test)
+mse = mean_squared_error(Y_test,prediction)
+rmse = np.sqrt(mse)
+rmse
+# scores_forest = cross_val_score(grid_search)
+#  cv=10)
+