Finished Model Evaluation Metrics

python/Supervised Learning/Model Evaluation Metrics/Regression Metrics/regression.py

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+from sklearn.datasets import load_boston
+from sklearn.model_selection import train_test_split
+import numpy as np
+import tests2 as t
+from sklearn.tree import DecisionTreeRegressor
+from sklearn.ensemble import RandomForestRegressor, AdaBoostRegressor
+from sklearn.linear_model import LinearRegression
+from sklearn.metrics import r2_score, mean_squared_error, mean_absolute_error
+
+boston = load_boston()
+y = boston.target
+X = boston.data
+
+X_train, X_test, y_train, y_test = train_test_split(
+    X, y, test_size=0.33, random_state=42)
+
+
+dec_tree = DecisionTreeRegressor()
+ran_for = RandomForestRegressor()
+ada = AdaBoostRegressor()
+lin_reg = LinearRegression()
+
+
+dec_tree.fit(X_train, y_train)
+ran_for.fit(X_train, y_train)
+ada.fit(X_train, y_train)
+lin_reg.fit(X_train, y_train)
+
+
+dec_pred = dec_tree.predict(X_test)
+ran_pred = ran_for.predict(X_test)
+ada_pred = ada.predict(X_test)
+lin_pred = lin_reg.predict(X_test)
+
+
+# potential model options
+a = 'regression'
+b = 'classification'
+c = 'both regression and classification'
+
+metrics_dict = {
+    'precision': b,
+    'recall': b,
+    'accuracy': b,
+    'r2_score': a,
+    'mean_squared_error': a,
+    'area_under_curve': b,
+    'mean_absolute_area': a
+}
+
+# checks your answer, no need to change this code
+t.q6_check(metrics_dict)
+print()
+
+models = {'dec_pred': dec_pred, 'ran_pred': ran_pred, 'ada_pred': ada_pred,
+          'lin_pred': lin_pred}
+metrics = [r2_score, mean_squared_error, mean_absolute_error]
+
+
+# Check r2
+def r2(actual, preds):
+    '''
+    INPUT:
+    actual - numpy array or pd series of actual y values
+    preds - numpy array or pd series of predicted y values
+    OUTPUT:
+    returns the r-squared score as a float
+    '''
+    sse = np.sum((actual - preds)**2)
+    sst = np.sum((actual - np.mean(actual))**2)
+    return 1 - sse / sst
+
+
+for i in models:
+    print(f'r2 manual for {i} is {r2(y_test, models[i]):.4f}')
+    print(f'r2 sklearn for {i} is {r2_score(y_test, models[i]):.4f}')
+    print()
+# Check solution matches sklearn
+
+
+def mse(actual, preds):
+    '''
+    INPUT:
+    actual - numpy array or pd series of actual y values
+    preds - numpy array or pd series of predicted y values
+    OUTPUT:
+    returns the mean squared error as a float
+    '''
+
+    return np.sum((actual - preds)**2) / len(actual)
+
+
+# Check your solution matches sklearn
+for i in models:
+    print(f'mse manual for {i} is {mse(y_test, models[i]):.4f}')
+    print(f'mse sklearn for {i} is'
+          f' {mean_squared_error(y_test, models[i]):.4f}')
+    print()
+
+
+def mae(actual, preds):
+    '''
+    INPUT:
+    actual - numpy array or pd series of actual y values
+    preds - numpy array or pd series of predicted y values
+    OUTPUT:
+    returns the mean absolute error as a float
+    '''
+
+    return np.sum(np.abs(actual - preds)) / len(actual)
+
+
+# Check your solution matches sklearn
+for i in models:
+    print(f'mae manual for {i} is {mae(y_test, models[i]):.4f}')
+    print(f'mae sklearn for {i} is'
+          f' {mean_absolute_error(y_test, models[i]):.4f}')
+    print()
+
+print('=================')
+print('Comparison of all models:\n')
+for i in models:
+    for j in range(len(metrics)):
+        print(f'{metrics[j].__name__} for '
+              f'{i} {metrics[j](y_test, models[i]):.4f}')
+    print()
+
+
+# match each metric to the model that performed best on it
+a = 'decision tree'
+b = 'random forest'
+c = 'adaptive boosting'
+d = 'linear regression'
+
+
+best_fit = {
+    'mse': b,
+    'r2': b,
+    'mae': b
+}
+
+# Tests your answer - don't change this code
+t.check_ten(best_fit)