sklearn中随机森林分类器RandomForestClassifier的实际应用

假设我们有一份CSV文件(以部分为例)：car_rf.csv

要用随机森林对其进行分类，其中最后一列视为标签，其余列视为特征

# coding = utf-8
import pandas as pd
from sklearn.ensemble import RandomForestClassifier
from IPython.display import Image
from sklearn import tree
import pydotplus

def read_dataset(fname = u"/car_rf.csv"):
    data = pd.read_csv(fname, index_col=0,encoding="utf-8",dtype=str)
    data = data.fillna(0)
    temp_col_list = ["",""] # ""中填特征的列名
    for i in temp_col_list:
        lables = data[i].unique().tolist()
        data[i] = data[i].apply(lambda n: lables.index(n))
    return data
train = read_dataset()

# ""中填标签的列名
y = train[""].values
X = train.drop([""], axis=1).values

rf = RandomForestClassifier(n_estimators=4, max_depth=2)
rf = rf.fit(X,y)

Estimators = rf.estimators_
for index, model in enumerate(Estimators):
    filename = str(index) + '.pdf'
    dot_data = tree.export_graphviz(model , out_file=None)
    graph = pydotplus.graph_from_dot_data(dot_data)
    Image(graph.create_png())
    graph.write_pdf(filename)