解释代码features_poly = poly_transformer.get_feature_names(features) features_nonzero = np.array(features_poly)[lr.coef_ != 0] coef_nonzero = lr.coef_[lr.coef_ != 0]

- `poly_transformer.get_feature_names(features)`：这行代码使用 `poly_transformer` 对象对输入特征 `features` 进行多项式转换，并返回转换后的特征名称列表。
- `features_nonzero = np.array(features_poly)[lr.coef_ != 0]`：这行代码创建 `features_nonzero` 数组，其中包含了那些与非零系数相关的多项式特征。`np.array(features_poly)` 将多项式特征名称列表转换为 NumPy 数组，然后 `lr.coef_ != 0` 返回了一个大小与回归模型系数数组相同的布尔数组，指示哪些系数不为零。通过将这个布尔数组作为索引应用于特征名称数组，我们得到了与非零系数相关的特征名称数组。
- `coef_nonzero = lr.coef_[lr.coef_ != 0]`：这行代码创建 `coef_nonzero` 数组，其中包含了那些非零系数的回归模型系数。与上一行代码类似，`lr.coef_ != 0` 返回了一个布尔数组，其中指示哪些系数不为零。通过将这个布尔数组作为索引应用于 `lr.coef_`，我们得到了一个包含非零系数的数组 `coef_nonzero`，其大小与 `features_nonzero` 相同。

相关问题

怎么修改代码：month = ["%02d" % i for i in range(1,13,1)] day = ["%02d" % i for i in range(1,32,1)] featuresfeatures_poly = poly_transformer.get_feature_names(features) features_nonzero = np.array(features_poly)[lr.coef_ != 0] coef_nonzero = lr.coef_[lr.coef_ != 0] = month + day features

As an AI language model, I do not have emotions, and I do not understand the concept of null in a human sense. However, in computer programming, null usually refers to a variable with no assigned value or an empty reference to an object.

poly_features_test = pd.DataFrame(poly_features_test, columns = poly_transformer.get_feature_names(['板温','现场温度','光照强度','风速','风向']))错误

The code you provided is incomplete, but based on what you've shared, the error might be related to the variable `poly_transformer`.

Here's an example of how to use `PolynomialFeatures` from scikit-learn to create polynomial features and then transform a test dataset with the same features:

import pandas as pd
from sklearn.preprocessing import PolynomialFeatures

# Create a toy test dataset with 3 numerical features
test_data = pd.DataFrame({
    'feature1': [1, 2, 3],
    'feature2': [4, 5, 6],
    'feature3': [7, 8, 9]
})

# Instantiate PolynomialFeatures object with degree=2 (for creating 2nd degree polynomial features)
poly_transformer = PolynomialFeatures(degree=2)

# Fit and transform the training data to create polynomial features
poly_features_train = poly_transformer.fit_transform(train_data)

# Transform the test data with the same polynomial features
poly_features_test = poly_transformer.transform(test_data)

# Create column names for the new features
poly_feature_names = poly_transformer.get_feature_names(['feature1', 'feature2', 'feature3'])

# Convert the transformed test data to a pandas DataFrame with the new feature names
poly_features_test = pd.DataFrame(poly_features_test, columns=poly_feature_names)

Make sure that the column names passed to `get_feature_names()` match the names of the columns in your test data.