我是一个新手,正在研究我的第一个真正的ML算法抱歉,如果这是重复的,但我找不到这样的答案。
我有以下数据帧(df
):
index Feature1 Feature2 Feature3 Target
001 01 01 03 0
002 03 03 01 1
003 03 02 02 1
我的代码如下:
data = df[['Feature1', 'Feature2', 'Feature3']]
labels = df['Target']
X_train, X_test, y_train, y_test = train_test_split(data, labels, test_size = 0.8)
clf = RandomForestClassifier().fit(X_train, y_train)
prediction_of_probability = clf.predict_proba(X_test)
我正在努力的是如何将
'prediction_of_probability'
恢复到数据帧中?我知道预测不是针对原始数据框中的所有项。
提前感谢你帮助我这样的新手!
最佳答案
您可以尝试保留列车的指标并进行测试,然后按以下方式组合起来:
from sklearn.ensemble import RandomForestClassifier
from sklearn.model_selection import train_test_split
data = df[['Feature1', 'Feature2', 'Feature3']]
labels = df['Target']
indices = df.index.values
# use the indices instead the labels to save the order of the split.
X_train, X_test,indices_train,indices_test = train_test_split(data,indices, test_size=0.33, random_state=42)
y_train, y_test = labels[indices_train], labels[indices_test]
clf = RandomForestClassifier().fit(X_train, y_train)
prediction_of_probability = clf.predict_proba(X_test)
然后你可以把概率放入新的
df_new
:>>> df_new = df.copy()
>>> df_new.loc[indices_test,'pred_test'] = prediction_of_probability # clf.predict_proba(X_test)
>>> print(df_new)
Feature1 Feature2 Feature3 Target pred_test
1 3 3 1 1 NaN
2 3 2 2 1 NaN
0 1 1 3 0 1.0
甚至对火车的预测:
>>> df_new.loc[indices_train,'pred_train'] = clf.predict_proba(X_train)
>>> print(df_new)
Feature1 Feature2 Feature3 Target pred_test pred_train
1 3 3 1 1 NaN 1.0
2 3 2 2 1 NaN 1.0
0 1 1 3 0 1.0 NaN
或者,如果您想混合train和test的概率,只需使用相同的列名(即
pred
)。