找到样本数量不一致的输入变量：RandomForestRegressor 的 [1, 4] 错误

Question

我指的是 this Random Forrest Algorithm example 来预测不同阶段的拒绝。

我正在从数据库中获取 stages 和 reject_count 的值。并为 x 使用 stages 值，为 y 使用 reject_count 值。我的代码是：

    import numpy as np
    from sklearn.ensemble import RandomForestRegressor
    from sklearn.model_selection import train_test_split
    from sklearn.preprocessing import StandardScaler

    stages = [102, 103, 104, 106]
    reject_count = [1, 3, 1, 2]
    li = []
    li.append(stages)
    l2 = []
    l2.append(reject_count)
    x = np.array(li)
    y = np.array(reject_count)
    x.shape
    y.shape

    X_train, X_test, y_train, y_test = train_test_split(x, y, test_size=0.2, random_state=0)
    print("===============")

    sc = StandardScaler()
    X_train = sc.fit_transform(X_train)
    X_test = sc.transform(X_test)

    regressor = RandomForestRegressor(n_estimators=100, random_state=0)
    print("x train", X_train)
    print("y train", y_train)
    regressor.fit(X_train, y_train)
    y_pred = regressor.predict(X_test)
    print(y_pred)

请指导我哪里做错了。

Answer 1

这里发生了两件事

首先你的 x 和 y 没有相同的维度，一个是列表的列表，另一个是列表。其次，假设您希望数据作为每个样本一个观察值的数组，您应该重塑 x 值。更多关于

import numpy as np
from sklearn.ensemble import RandomForestRegressor
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler

stages = [102, 103, 104, 106]
reject_count = [1, 3, 1, 2]
#li = []
#li.append(stages)
#l2 = []
#l2.append(reject_count)
x = np.array(stages).reshape(-1, 1)
y = np.array(reject_count)

print(x, y)
x.shape
y.shape

X_train, X_test, y_train, y_test = train_test_split(x, y, test_size=0.2, random_state=0)
print("===============")

sc = StandardScaler()
X_train = sc.fit_transform(X_train)
X_test = sc.transform(X_test)

regressor = RandomForestRegressor(n_estimators=100, random_state=0)
print("x train", X_train)
print("y train", y_train)
regressor.fit(X_train, y_train)
y_pred = regressor.predict(X_test)
print(y_pred)

找到样本数量不一致的输入变量：RandomForestRegressor 的 [1, 4] 错误

Getting found input variables with inconsistent numbers of samples: [1, 4] error for RandomForestRegressor

machine-learning

python-3.x

random-forest

scikit-learn