如何将数据输入随机森林分类器并查看预测

Question

我已经使用 scikit learn 和 python 构建了一个随机森林分类器，但我在实际输入数据以查看预测时遇到了麻烦。我想查看输出的格式，并将其转换为 json 文件。我附上了随机森林的代码和数据的样子。我相信我需要使用 'y_pred'，但我不确定输入数据需要采用什么格式。

X = dataset.iloc[:, 2:4].values
y = dataset["pages"]
from sklearn.model_selection import train_test_split
X_train,X_test,y_train,y_test = train_test_split(X,y,test_size=0.2,random_state=0)
from sklearn.preprocessing import StandardScaler
sc = StandardScaler()
X_train = sc.fit_transform(X_train)
X_test = sc.transform(X_test)
from sklearn.ensemble import RandomForestClassifier
classifier = RandomForestClassifier(n_estimators=20, random_state = 0)
classifier = classifier.fit(X_train,y_train)
y_pred = classifier.predict(X_test)
from sklearn.metrics import classification_report, confusion_matrix, accuracy_score

Answer 1

您可以简单地将预测值与特征矩阵连接起来。

另请注意，管道正是为此目的，当您首先要转换数据然后应用一些分类器时。

这应该适合你：

from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler
from sklearn.pipeline import make_pipeline
from sklearn.ensemble import RandomForestClassifier
import pandas as pd

X = dataset.iloc[:, 2:4].values
y = dataset["pages"]

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=0)
classifier = make_pipeline(StandardScaler(), RandomForestClassifier(n_estimators=20, random_state=0))
classifier = classifier.fit(X_train, y_train)
y_pred = classifier.predict(X_test)

pred = pd.concat([X_test, pd.Series(y_pred, name="pages")], axis=1)