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Author: Algorithmica

2019-october/3.machine learning introduction/model evaluation.py

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+import pandas as pd
+import os
+from sklearn import tree, model_selection
+import io
+import pydot
+
+dir = 'E:/'
+titanic_train = pd.read_csv(os.path.join(dir, 'train.csv'))
+print(titanic_train.info())
+print(titanic_train.columns)
+
+X_train = titanic_train[ ['SibSp', 'Parch'] ]
+y_train = titanic_train['Survived']
+dt_estimator = tree.DecisionTreeClassifier()
+dt_estimator.fit(X_train, y_train)
+print(dt_estimator.tree_)
+model_selection.cross_val_score(dt_estimator, X_train, y_train, scoring="accuracy", cv=5).mean()
+
+#visualize the deciion tree
+dot_data = io.StringIO() 
+tree.export_graphviz(dt_estimator, out_file = dot_data, feature_names = X_train.columns)
+graph = pydot.graph_from_dot_data(dot_data.getvalue())[0] 
+dir = 'E:/'
+graph.write_pdf(os.path.join(dir, "tree.pdf"))
+
+titanic_test = pd.read_csv(os.path.join(dir, 'test.csv'))
+print(titanic_test.info())
+X_test = titanic_test[ ['SibSp', 'Parch'] ]
+titanic_test['Survived'] = dt_estimator.predict(X_test)
+titanic_test.to_csv(os.path.join(dir, 'submission.csv'), columns=['PassengerId', 'Survived'], index=False)