Please enable JavaScript.

Coggle requires JavaScript to display documents.

Feature preprocessing and Piplines (Categorical Data (Dummy variables (pd…

- - - - MinMaxScaler
        from sklearn.preprocessing import MinMaxScaler
    - - Scale
        from sklearn.preprocessing import scale
        X_scaled = scale(X)
        np.mean(X), np.std(X)
        np.mean(X_scaled), np.std(X_scaled)
      - StandardScaler
        from sklearn.preprocessing import StandardScaler
        steps = [('scaler', StandardScaler()), ('knn', KNeighborsClassifier())]
        pipeline = Pipeline(steps)
        X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=21)
        knn_scaled = pipeline.fit(X_train, y_train)
        y_pred = pipeline.predict(X_test)
        accuracy_score(y_test, y_pred)
        knn_unscaled = KNeighborsClassifier().fit(X_train, y_train)
        knn_unscaled.score(X_test, y_test)