2、抽取 iris 中的数据训练一个感知器模型 View Code 3、编写一个可视化决策区域的函数 importnumpy as np, pandas as pdimportmatplotlib.pyplot as pltfrommatplotlib.colorsimportListedColormapdefplotDecisionRegions(X, y, classifier, resolution=0.02):#定义颜色和标记符号,通过颜色列图表生成颜色示例图marker =...
lr=lr.fit(X_train_lda,y_train) plot_decision_regions(X_train_lda,y_train,classifier=lr) plt.xlabel('LD 1') plt.ylabel('LD 2') plt.legend(loc='lower left') plt.show() x_test_lda=lda.transform(X_test_std) plot_decision_regions(x_test_lda,y_test,classifier=lr) plt.xlabel('LD...
def plot_decision_regions(X, y, classifier, resolution=0.02): # setup marker generator and color map markers = ('s', 'x', 'o', '^', 'v') colors = ('red', 'blue', 'lightgreen', 'gray', 'cyan') cmap = ListedColormap(colors[:len(np.unique(y))]) # plot the decision surf...
lr = lr.fit(X_train_lda, y_train) plot_decision_regions(X_train_lda, y_train, classifier=lr) plt.xlabel('LD1') plt.ylabel('LD2') plt.legend(loc='lower left') plt.show() 结果: 图12 从结果图像中可以看到,logistic回归模型只错误的判断了类别2中的一个样本 通过降低正则化强度来对决策...
LightGBM(Light Gradient Boosting Machine)是一个基于梯度提升决策树(Gradient Boosting Decision Tree, GBDT)的机器学习模型,由微软研究院开发。它是对GBDT算法的优化和高效实现,旨在解决大数据量级下的训练问题,特别适用于工业实践...
And that's it - you've just plotted the decision boundaries using Python and Scikit-Learn! But, if you are interested to take a look at a few more examples - continue reading! Note: You can also reduce the data dimensions to 2 with a method such as PCA and then plot the model's ...
但是Adaboost的stump仅仅是按照准确率来了,而decision tree的标准是purity,纯净度。意思就是熵了。purifying的核心思想就是每次切割都尽可能让左子树和右子树中同类样本占得比例最大或者yn都很接近(regression),即错误率最小。比如说classifiacation问题中,如果左子树全是正样本,右子树全是负样本,那么它的纯净度就...
决策树/范例三: Plot the decision surface of a decision tree on the iris dataset http://scikit-learn.org/stable/auto_examples/tree/plot_iris.html 此范例利用决策树分类器将资料集进行分类,找出各类别的分类边界。以鸢尾花资料集当作范例,每次取两个特征做训练,个别绘制不同品种的鸢尾花特征的分布范围。
Business Intelligence: You can automate Tableau dashboard using Python to prepare interactive dashboards and visualizations to help you in business decision-making. Advanced Data Transformations: Using Python in Tableau lets you perform complex calculations, create custom aggregations, and manipulate data...
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