首先导入数据，将数据分为训练集和测试集：
import pandas as pd # Load data melbourne_file_path = ‘../input/melbourne-housing-snapshot/melb_data.csv‘ melbourne_data = pd.read_csv(melbourne_file_path) # Filter rows with missing values melbourne_data = melbourne_data.dropna(axis=0) # Choose target and features y = melbourne_data.Price melbourne_features = [‘Rooms‘, ‘Bathroom‘, ‘Landsize‘, ‘BuildingArea‘, ‘YearBuilt‘, ‘Lattitude‘, ‘Longtitude‘] X = melbourne_data[melbourne_features] from sklearn.model_selection import train_test_split # split data into training and validation data, for both features and target # The split is based on a random number generator. Supplying a numeric value to # the random_state argument guarantees we get the same split every time we # run this script. train_X, val_X, train_y, val_y = train_test_split(X, y,random_state = 0)
导入sklearn当中随机森立算法实现的包，拟合模型并求出平均误差：
from sklearn.ensemble import RandomForestRegressor from sklearn.metrics import mean_absolute_error forest_model = RandomForestRegressor(random_state=1) forest_model.fit(train_X, train_y) melb_preds = forest_model.predict(val_X) print(mean_absolute_error(val_y, melb_preds))
输出：
202888.18157951365 /opt/conda/lib/python3.6/site-packages/sklearn/ensemble/forest.py:245: FutureWarning: The default value of n_estimators will change from 10 in version 0.20 to 100 in 0.22. "10 in version 0.20 to 100 in 0.22.", FutureWarning)
得解。
原文：https://www.cnblogs.com/geeksongs/p/12637595.html