使我所有的预测都偏向于二进制分类 [英] Having all my predictions inclined to one side for binary classification

问题描述

我正在训练一个模型，该模型包含8个功能，可以让我们预测房间出售的可能性.

I was training a model that contains 8 features that allow us to predict the probability of a room been sold.

• 区域:房间所属的区域(整数，取值介于1到10之间)

• Region: The region the room belongs to (an integer, taking a value between 1 and 10)

日期:停留日期(1-365之间的整数，这里我们只考虑一日请求)

Date: The date of stay (an integer between 1‐365, here we consider only one‐day request)

工作日:星期几(1到7之间的整数)

Weekday: Day of week (an integer between 1‐7)

公寓:房间是整个公寓(1)还是仅一个房间(0)

Apartment: Whether the room is a whole apartment (1) or just a room (0)

#beds:房间中的床位数(1-4之间的整数)

#beds:The number of beds in the room (an integer between 1‐4)

评论:卖家的平均评论(介于1到5之间的连续变量)

Review: Average review of the seller (a continuous variable between 1 and 5)

图片质量:房间图片的质量(0到1之间的连续变量)

Pic Quality: Quality of the picture of the room (a continuous variable between 0 and 1)

价格:房间的历史发布价格(一个连续变量)

Price: he historic posted price of the room (a continuous variable)

接受:该帖子最后是否被接受(有人接受了，为1)或不接受(0)*

Accept: Whether this post gets accepted (someone took it, 1) or not (0) in the end*

列Accept是"y".因此，这是一个二进制分类.

Column Accept is the "y". Hence, this is a binary classification.

1. 我已经为分类数据完成了 OneHotEncoder .
2. 我已对数据进行归一化.
3. 我修改了以下 RandomRofrest 参数:

• 最大深度:峰值为16
• n_estimators :峰值为300
• min_samples_leaf :高峰在2
• max_features :对AUC没有影响.
• Max_depth: Peak at 16
• n_estimators: Peak at 300
• min_samples_leaf: Peak at 2
• max_features: Has no effect on the AUC.

AUC达到0.7889的峰值.我还能做些什么来增加它?

The AUC peaked at 0.7889. What else can I do to increase it?

这是我的代码

import pandas as pd
import numpy as np
from sklearn.model_selection import cross_val_score
from sklearn.preprocessing import OneHotEncoder
from sklearn.pipeline import make_pipeline
from sklearn.compose import make_column_transformer
from sklearn.model_selection import train_test_split
df_train = pd.read_csv('case2_training.csv')

# Exclude ID since it is not a feature
X, y = df_train.iloc[:, 1:-1], df_train.iloc[:, -1]
y = y.astype(np.float32)

# Split the data
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.05,shuffle=False)

ohe = OneHotEncoder(sparse = False)
column_trans = make_column_transformer(
(OneHotEncoder(),['Region','Weekday','Apartment']),remainder='passthrough')
X_train = column_trans.fit_transform(X_train)
X_test = column_trans.fit_transform(X_test)

# Normalization
from sklearn.preprocessing import MaxAbsScaler
mabsc = MaxAbsScaler()

X_train = mabsc.fit_transform(X_train)
X_test = mabsc.transform(X_test)

X_train = X_train.astype(np.float32)
X_test = X_test.astype(np.float32)

from sklearn.ensemble import RandomForestClassifier
from sklearn.metrics import roc_auc_score

RF =  RandomForestClassifier(min_samples_leaf=2,random_state=0, n_estimators=300,max_depth = 16,n_jobs=-1,oob_score=True,max_features=i)
cross_val_score(RF,X_train,y_train,cv=5,scoring = 'roc_auc').mean()
RF.fit(X_train, y_train)
yhat = RF.predict_proba(X_test)

print("AUC:",roc_auc_score(y_test, yhat[:,-1]))

# Run the prediction on the given test set.
testset = pd.read_csv('case2_testing.csv')
testset = testset.iloc[:, 1:] # exclude the 'ID' column
testset = column_trans.fit_transform(testset)
testset = mabsc.transform(testset)


yhat_2 = RF.predict_proba(testset)
final_prediction = yhat[:,-1]

但是，所有来自"final_prediction"的概率都低于0.45，基本上，该模型认为所有样本均为0.有人可以帮忙吗?

However, all the probabilities from 'final_prediction` are below 0.45, basically, the model believes that all the samples are 0. Can anyone help ?

推荐答案

您正在测试集上使用 column_trans.fit_transform ，这将完全覆盖训练期间安装的功能.基本上，数据现在采用的是您的训练模型无法理解的格式.

You are using column_trans.fit_transform on the test set, this completely overwrites the features that were fitted during training. Basically the data is now in a format your trained model doesn't understand.

在对训练集进行训练后，只需使用 column_trans.transform .

Once fitted in training on the training set, simply use column_trans.transform afterwards.

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