在Matplotlib中,我们可以使用plot_decision_boundary函数和plt.contourf函数来绘制决策边界。首先,我们来了解一下plot_decision_boundary函数。这个函数是Scikit-learn库中的一个函数,专门用于绘制决策边界。它需要传入分类器的决策函数、特征值和目标值作为参数。具体用法如下: from sklearn.datasets import make_classificatio...
For each pair of iris features, the decision tree learns decision boundaries made of combinations of simple thresholding rules inferred from the training samples. We also show the tree structure of a model built on all of the features. In [10]: importnumpyasnpimportmatplotlib.pyplotaspltfromskl...
DecisionTreeClassifier建立决策树分类器。 importnumpyasnp importmatplotlib.pyplotasplt fromsklearn.datasetsimportload_iris fromsklearn.treeimportDecisionTreeClassifier iris=load_iris() forpairidx,pairinenumerate([[0,1],[0,2],[0,3], [1,2],[1,3],[2,3]]): X=iris.data[:,pair] y=iris.ta...
显示决策边界使用 inspection.DecisionBoundaryDisplay 显示决策边界 如果使用 Matplotlib 来绘制,会很麻烦: 使用np.linspace 设置坐标范围; 使用plt.meshgrid 计算网格; 使用plt.contourf 绘制决策边界填充; 然后使用 plt.scatter 绘制数据点。 现在,使用 inspection.DecisionBoundaryDisplay 可以简化这一过程:...
master highdimensional-decision-boundary-plot/decisionboundaryplot.py / Jump to Go to file 1003 lines (887 sloc) 41.6 KB Raw Blame import numpy as np import matplotlib.pyplot as mplt import os from sklearn.base import BaseEstimator
To use matplotlib, we must install it first using the following command. #Python 3.x pip install matplotlib Use Bar Plot to Visualize CSV Data A bar plot is a graph that contains rectangular bars that display the numeric values for categorical feature levels as bars. We will use the bar...
import matplotlib.pyplot as plt from sklearn import metrics from sklearn.cluster import KMeans from sklearn.datasets import load_digits from sklearn.decomposition import PCA from sklearn.preprocessing import scale np.random.seed(42) digits = load_digits() data = scale(digits.data) n_samples, ...