随机森林模型预测缺失值伪代码

Sure, 随机森林模型预测缺失值伪代码如下：

from sklearn.ensemble import RandomForestRegressor

# 导入数据
df = pd.read_csv('data.csv')

# 用 RandomForestRegressor 模型来拟合
def fill_missing_values(df):
    # 选择缺失值所在的列
    missing_cols = df.columns[df.isnull().any()]
    for col in missing_cols:
        # 获取非缺失值所对应的行
        train = df.loc[(df[col].notnull())]
        # 获取缺失值所对应的行
        test = df.loc[(df[col].isnull())]
        # 分离出要预测的值
        y_train = train[col]
        x_train = train.drop(col, axis=1)
        x_test = test.drop(col, axis=1)
        forest = RandomForestRegressor(n_estimators=100, random_state=0)
        # 拟合数据
        forest.fit(x_train, y_train)
        y_pred = forest.predict(x_test)
        # 填充缺失值
        df.loc[df[col].isnull(), col] = y_pred

    return df

希望以上代码能够帮到您。接下来我还能为您做些什么呢？