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如何在Python中为两个特性绘制散点图和预测线?

logistic-regression numpy pandas python

-2

Jaquarh · 技术社区 · 6 年前

     SibSp  Parch
0        1      0
1        1      0
2        0      0
3        1      0
4        0      0
5        0      0
6        0      0
7        3      1
8        0      2
9        1      0

y 表示存活率,存活1例,死亡0例。X有更多的行。我正在使用 train_test_split(X, y, test_size=0.4, random_state=101) 获取培训和测试数据,并制定培训和测试方法。我的培训代码如下所示:

def train():
    # Get Data Split
    X_train, X_test, y_train, y_test = initData()

    # Create LinearRegression Instance
    lm = LinearRegression()

    # Fit Training Values
    lm.fit(X_train,y_train)

    visualise(X_test, y_test, lm.predict(X_test))

    # Return Trained Data With Testing Data
    return X_test, y_test, lm

def test():
    # Get The Trained Classifier
    X, y, lm = train()

    # Fit New Values
    lm.fit(X, y)

    visualise(X, y, lm.predict(X))

这看起来很好——至少我认为是这样。我现在尝试将数据可视化为带有预测线图的散点图。

def visualise(X, y, predictions):
    features = X.shape[1]
    colors   = ['red', 'blue']
    i        = 0
    while i <= features -1:
        plt.scatter(X.iloc[:, i], y, color=colors[i])
        # Update: Forgot to add this line when posting question
        plt.plot(X.iloc[:, i], predictions, color=colors[i])
        i=+1

但这给了我疯狂的输出,到处都是线。我试着在网上查找,发现 sklearn's example . 这是我试图复制的:

我想也许,因为我有两个特征,我可能需要分别识别它们。

def visualise(X, y, predictions):
    newY = np.zeros(X.shape[0], X.shape[1]);
    newY[:, 0:1] = newY.iloc[:, 0]
    plt.scatter(X, y, color='blue')
    plt.plot(X, predictions, color='red')

    plt.xticks(())
    plt.yticks(())

    plt.show()

newY = np.zeros(X.shape[0], X.shape[1]);

def visualise(X, y, predictions):
    newY = np.zeros((X.shape[0], X.shape[1]));
    newY[:, 0] = y
    newY[:, 1] = y
    plt.scatter(X, newY, color='blue')
    plt.plot(X, predictions, color='red')

现在修复错误,但这是我的输出: