Day.32

查看官方文档：

import pandas as pd

from sklearn.datasets import load_iris

from sklearn.model_selection import train_test_split

from sklearn.ensemble import RandomForestClassifier

iris = load_iris()

df = pd.DataFrame(iris.data, columns=iris.feature_names)

df['target'] = iris.target  

features = iris.feature_names 

target = 'target'

X_train, X_test, y_train, y_test = train_test_split(

    df[features], df[target], test_size=0.2, random_state=42

)

model = RandomForestClassifier(n_estimators=100, random_state=42)

model.fit(X_train, y_train)

选择特征：

feature = 'petal length (cm)'

feature_name = feature

feature = 'petal length (cm)'

feature_name = feature  

target_plot = TargetPlot(

    df=df,  

    feature=feature,  

    feature_name=feature_name,  

    # target='target',  

    target='target',  

    grid_type='percentile', 

    num_grid_points=10 

)

绘图：

fig, axes, summary_df = target_plot.plot(

    which_classes=None, 

    show_percentile=True,  

    engine='plotly',

    template='plotly_white'

)

fig.update_layout(

    width=800,  

    height=500,  

    title=dict(text=f'Target Plot: {feature_name}', x=0.5)  

)@浙大疏锦行

fig.show()