模型的特征数量必须与输入匹配

Question

由于某些原因，该数据集的特征被解释为行，"Model n_features为16，input n_features.为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？

示例培训日期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

您有一个小的拼写错误-您创建了变量xTest，然后立即覆盖到不正确的内容。将违规行更改为：

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