模型的特征数量必须与输入匹配

Question

出于某种原因，此数据集的特征被解释为行，"Model n_features is 16 and input n_features is 18189" 其中 18189 是行数，16 是正确的特征列表。

可疑代码在这里：

for var in cat_cols:
    num = LabelEncoder()
    train[var] = num.fit_transform(train[var].astype('str'))
    train['output'] = num.fit_transform(train['output'].astype('str'))

for var in cat_cols:
    num = LabelEncoder()
    test[var] = num.fit_transform(test[var].astype('str'))
    test['output'] = num.fit_transform(test['output'].astype('str'))


clf = RandomForestClassifier(n_estimators = 10)

xTrain = train[list(features)].values
yTrain = train["output"].values

xTest = test[list(features)].values
xTest = test["output"].values 

clf.fit(xTrain,yTrain)
clfProbs = clf.predict(xTest)#Error happens here.

有人有什么想法吗？

样本训练日期 csv

tr4,42,"JobCat4","divorced","tertiary","yes",2,"yes","no","unknown",5,"may",0,1,-1,0,"unknown","TypeA"

样本测试数据csv

tst2,47,"JobCat3","married","unknown","no",1506,"yes","no","unknown",5,"may",0,1,-1,0,"unknown",?

Answer 1

您有一个小错字 - 您创建了变量 xTest，然后立即覆盖了不正确的内容。将违规行更改为：

xTest = test[list(features)].values
yTest = test["output"].values

模型的特征数量必须与输入匹配

Number of features of the model must match the input

python

numpy

scikit-learn

sklearn-pandas