Finished Model Evaluation Metrics

python/Supervised Learning/Model Evaluation Metrics/3. Testing your models/quiz.py

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+# Import statements
+from sklearn.tree import DecisionTreeClassifier
+from sklearn.metrics import accuracy_score
+import pandas as pd
+import numpy as np
+from sklearn.model_selection import train_test_split
+
+# Import the train test split
+# http://scikit-learn.org/0.16/modules/generated/sklearn.cross_validation.train_test_split.html
+
+
+# Read in the data.
+data = np.asarray(pd.read_csv('data.csv', header=None))
+# Assign the features to the variable X, and the labels to the variable y.
+X = data[:, 0:2]
+y = data[:, 2]
+
+# Use train test split to split your data
+# Use a test size of 25% and a random state of 42
+X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.25,
+                                                    random_state=42)
+
+# Instantiate your decision tree model
+model = DecisionTreeClassifier()
+
+# TODO: Fit the model to the training data.
+model.fit(X_train, y_train)
+
+# TODO: Make predictions on the test data
+y_pred = model.predict(X_test)
+
+# TODO: Calculate the accuracy and assign it to the variable acc on the test
+# data.
+acc = accuracy_score(y_test, y_pred)
+print(acc)