特征工程与选择

端到端机器学习

Joshua Stapleton

Machine Learning Engineer

特征工程

创建特征

简化问题
提升模型效率

技术

修改现有特征
设计新特征

收益

更易部署、维护、训练
提高可解释性

机器学习生命周期当前阶段：特征工程

归一化

将数值特征缩放到 [0, 1]
当特征量纲/范围不同尤其有用

from sklearn.model_selection import train_test_split
from sklearn.preprocessing import Normalizer

# Split the data
X_train, X_test = train_test_split(df, test_size=0.2, random_state=42)
# Createnormalizer object, fit on training data, normalize, and transform test set
norm = Normalizer()
X_train_norm = norm.fit_transform(X_train)
X_test_norm = norm.transform(X_test)

标准化

将数据缩放为均值=0、方差=1
适用于假设均值与方差相近的算法

from sklearn.preprocessing import StandardScaler

# Split the data
X_train, X_test = train_test_split(df, test_size=0.2, random_state=42)
# Create a scaler object and fit training data to standardize it
sc = StandardScaler()
X_train_stzd = sc.fit_transform(X_train)
# Only standardize the test data
X_test_stzd = sc.transform(X_test)

什么是好的特征？

使用相关特征
预约当天的天气不应影响诊断

一张暴风雨天气的图片，展示特征选择中的相关性原则

使用不相似（正交）的特征
以月为单位的年龄与以年为单位的年龄冗余

一个展示特征选择中正交性原则的图示

sklearn.feature_selection

from sklearn.ensemble import RandomForestClassifier
from sklearn.feature_selection import SelectFromModel
from sklearn.model_selection import train_test_split


# Splitting data into train and test subsets first to avoid data leakage
X_train, X_test, y_train, y_test = train_test_split(
    heart_disease_df_X, heart_disease_df_y, test_size=0.2, random_state=42)

sklearn.feature_selection（续）

# Define and fit the random forest model
rf = RandomForestClassifier(n_jobs=-1, class_weight='balanced', max_depth=5)
rf.fit(X_train, y_train)


# Define and run feature selection
model = SelectFromModel(rf, prefit=True)
features_bool = model.get_support()
features = heart_disease_df.columns[features_bool]

Vamos praticar!

端到端机器学习