我已经应用了随机森林分类器来获取数据集中特定行贡献的特征。然而，我得到了两个特征值，而不是一个。我不太确定为什么会这样。以下是我的代码。
import numpy as npimport pandas as pdfrom sklearn.datasets import make_classificationfrom sklearn.ensemble import RandomForestClassifierfrom treeinterpreter import treeinterpreter as tifrom treeinterpreter import treeinterpreter as tiX, y = make_classification(n_samples=1000, n_features=6, n_informative=3, n_classes=2, random_state=0, shuffle=False)# Creating a dataFramedf = pd.DataFrame({'Feature 1':X[:,0], 'Feature 2':X[:,1], 'Feature 3':X[:,2], 'Feature 4':X[:,3], 'Feature 5':X[:,4], 'Feature 6':X[:,5], 'Class':y})y_train = df['Class']X_train = df.drop('Class',axis = 1)rf = RandomForestClassifier(n_estimators=50, random_state=0)rf.fit(X_train, y_train)print ("-"*20) importances = rf.feature_importances_indices = X_train.columnsinstances = X_train.loc[[60]]print(rf.predict(instances))print ("-"*20) prediction, biases, contributions = ti.predict(rf, instances)for i in range(len(instances)): print ("Instance", i) print ("-"*20) print ("Bias (trainset mean)", biases[i]) print ("-"*20) print ("Feature contributions:") print ("-"*20) for c, feature in sorted(zip(contributions[i], indices), key=lambda x: ~abs(x[0].any())): print (feature, np.round(c, 3)) print ("-"*20) 这是我的代码的输出。有人能解释为什么偏差和特征输出两个值而不是一个吗？
--------------------[0]--------------------Instance 0--------------------Bias (trainset mean) [ 0.49854 0.50146]--------------------Feature contributions:--------------------Feature 1 [ 0.16 -0.16]Feature 2 [-0.024 0.024]Feature 3 [-0.154 0.154]Feature 4 [ 0.172 -0.172]Feature 5 [ 0.029 -0.029]Feature 6 [ 0.019 -0.019]回答：
你得到长度为2的偏差和特征贡献数组的原因非常简单，因为你有一个2类分类问题。
正如包创建者在这篇博客文章中清楚解释的，在鸢尾花数据集的3类情况下，你会得到长度为3的数组（即每个类一个数组元素）：
from treeinterpreter import treeinterpreter as tiimport numpy as npfrom sklearn.ensemble import RandomForestClassifierfrom sklearn.datasets import load_irisiris = load_iris()rf = RandomForestClassifier(max_depth = 4)idx = range(len(iris.target))np.random.shuffle(idx)rf.fit(iris.data[idx][:100], iris.target[idx][:100])prediction, bias, contributions = ti.predict(rf, instance)print "Prediction", predictionprint "Bias (trainset prior)", biasprint "Feature contributions:"for c, feature in zip(contributions[0], iris.feature_names): print feature, c这将给出：
Prediction [[ 0. 0.9 0.1]]Bias (trainset prior) [[ 0.36 0.262 0.378]]Feature contributions:sepal length (cm) [-0.1228614 0.07971035 0.04315104]sepal width (cm) [ 0. -0.01352012 0.01352012]petal length (cm) [-0.11716058 0.24709886 -0.12993828]petal width (cm) [-0.11997802 0.32471091 -0.20473289]公式
prediction = bias + feature_1_contribution + ... + feature_n_contribution来自TreeInterpreter适用于分类问题中的每个类；因此，对于k类分类问题，相应的数组将长度为k（在你的例子中k=2，而对于鸢尾花数据集k=3）。
