def visualizer(classifier_name, classifier, X, y, set_type):
    # Importing libraries
    import numpy as np
    import matplotlib.pyplot as plt
    
    # Visualising the Training set results
    from matplotlib.colors import ListedColormap
    X_set, y_set = X, y
    X1, X2 = np.meshgrid(np.arange(start = X_set[:, 0].min() - 1, stop = X_set[:, 0].max() + 1, step = 0.01),
                        np.arange(start = X_set[:, 1].min() - 1, stop = X_set[:, 1].max() + 1, step = 0.01))
    plt.contourf(X1, X2, classifier.predict(np.array([X1.ravel(), X2.ravel()]).T).reshape(X1.shape),
                alpha = 0.75, cmap = ListedColormap(('red', 'green')))
    plt.xlim(X1.min(), X1.max())
    plt.ylim(X2.min(), X2.max())
    for i, j in enumerate(np.unique(y_set)):
        plt.scatter(X_set[y_set == j, 0], X_set[y_set == j, 1],
                    c = ListedColormap(('red', 'green'))(i), label = j)
    plt.title(f'{classifier_name} ({set_type} set)')
    plt.xlabel('Age')
    plt.ylabel('Estimated Salary')
    plt.legend()
    plt.show()
0ef93e56a1bf6af3e5c7e978279dc81bfa3407a0
<mask token>

def visualizer(classifier_name, classifier, X, y, set_type):
    import numpy as np
    import matplotlib.pyplot as plt
    from matplotlib.colors import ListedColormap
    X_set, y_set = X, y
    X1, X2 = np.meshgrid(np.arange(start=X_set[:, 0].min() - 1, stop=X_set[
        :, 0].max() + 1, step=0.01), np.arange(start=X_set[:, 1].min() - 1,
        stop=X_set[:, 1].max() + 1, step=0.01))
    plt.contourf(X1, X2, classifier.predict(np.array([X1.ravel(), X2.ravel(
        )]).T).reshape(X1.shape), alpha=0.75, cmap=ListedColormap(('red',
        'green')))
    plt.xlim(X1.min(), X1.max())
    plt.ylim(X2.min(), X2.max())
    for i, j in enumerate(np.unique(y_set)):
        plt.scatter(X_set[y_set == j, 0], X_set[y_set == j, 1], c=
            ListedColormap(('red', 'green'))(i), label=j)
    plt.title(f'{classifier_name} ({set_type} set)')
    plt.xlabel('Age')
    plt.ylabel('Estimated Salary')
    plt.legend()
    plt.show()

def visualizer(classifier_name, classifier, X, y, set_type):
    # Importing libraries
    import numpy as np
    import matplotlib.pyplot as plt
    
    # Visualising the Training set results
    from matplotlib.colors import ListedColormap
    X_set, y_set = X, y
    X1, X2 = np.meshgrid(np.arange(start = X_set[:, 0].min() - 1, stop = X_set[:, 0].max() + 1, step = 0.01),
                        np.arange(start = X_set[:, 1].min() - 1, stop = X_set[:, 1].max() + 1, step = 0.01))
    plt.contourf(X1, X2, classifier.predict(np.array([X1.ravel(), X2.ravel()]).T).reshape(X1.shape),
                alpha = 0.75, cmap = ListedColormap(('red', 'green')))
    plt.xlim(X1.min(), X1.max())
    plt.ylim(X2.min(), X2.max())
    for i, j in enumerate(np.unique(y_set)):
        plt.scatter(X_set[y_set == j, 0], X_set[y_set == j, 1],
                    c = ListedColormap(('red', 'green'))(i), label = j)
    plt.title(f'{classifier_name} ({set_type} set)')
    plt.xlabel('Age')
    plt.ylabel('Estimated Salary')
    plt.legend()
    plt.show()