Methods and practices of scikit-learn processing missing data

scikit-learn handles missing data: Methods and practice

In data analytics and machine learning projects, processing missing data is a common and critical task. scikit-learn (sklearn for short), as a widely used machine learning library in Python, provides a variety of tools and techniques to help us deal with missing data. This article will introduce in detail how sklearn handles missing data and provide actual code examples.

The challenge of missing data

In real-world datasets, missing data is inevitable. Missing data can be random or may be caused by some identifiable reasons. The challenge of handling missing data is:

Missing data may lead to bias in the data set, affecting the accuracy of the analysis results.
Machine learning algorithms usually cannot directly deal with missing values.
Inappropriate handling may lead to loss of information.

Method of sklearn for processing missing data

sklearn provides a variety of ways to process missing data, including deletion, fill, and prediction.

Delete missing data

The easiest way to deal with it is to delete rows or columns containing missing values. This method is suitable for the following situations:

There are very few missing values.
The data set is very large, and deleting missing values has little effect on the results.

from  import SimpleImputer

# Create a dataset containing missing valuesdata = ({
    'A': [1, 2, None, 4],
    'B': [None, 2, 3, 4]
})

# Delete rows with missing values(inplace=True)

Fill in missing data

If deleting missing values is not feasible, you can use the fill method.SimpleImputerThe class provides a variety of fill strategies:

Mean fill

Use the mean of the column to fill in the missing values, which is suitable for numerical data.

imputer = SimpleImputer(strategy='mean')
data['A'] = imputer.fit_transform(data[['A']])

Median fill

Use the median of the column to fill in the missing values, which are insensitive to outliers.

imputer = SimpleImputer(strategy='median')
data['A'] = imputer.fit_transform(data[['A']])

Constant padding

Use a constant to fill all missing values.

imputer = SimpleImputer(strategy='constant', fill_value=0)
data['A'] = imputer.fit_transform(data[['A']])

Most frequent value filling

Fill in missing values with the most frequently occurring values in the column.

imputer = SimpleImputer(strategy='most_frequent')
data['A'] = imputer.fit_transform(data[['A']])

Predict missing data

For more complex datasets, missing values can be predicted using machine learning models.

K-Nearest Neighbor (KNN) Fill

useKNNImputerClass, predict missing values based on K-nearest neighbor algorithm.

from  import KNNImputer

imputer = KNNImputer(n_neighbors=2)
data[['A', 'B']] = imputer.fit_transform(data[['A', 'B']])

Handle missing values of classified data

For classified data, you can useSimpleImputerofmost_frequentStrategy orKNNImputer。

data = ({
    'C': ['apple', 'banana', None, 'banana'],
    'D': [None, 'orange', 'apple', 'banana']
})

imputer = SimpleImputer(strategy='most_frequent')
data['C'] = imputer.fit_transform(data[['C']])

Process multivariate data

When there are multiple variables in the dataset, you can useIterativeImputer, it uses an iterative method to fill in missing values.

from  import enable_iterative_imputer
from  import IterativeImputer

imputer = IterativeImputer()
data[['A', 'B', 'C', 'D']] = imputer.fit_transform(data[['A', 'B', 'C', 'D']])

Evaluate the filling effect

After filling the missing values, it is necessary to evaluate the impact of the filling effect on the performance of the model. Cross-validation and different evaluation metrics can be used to evaluate.

from sklearn.model_selection import cross_val_score
from  import DecisionTreeClassifier

model = DecisionTreeClassifier()
scores = cross_val_score(model, data, cv=5)
print("Accuracy: %0.2f (+/- %0.2f)" % ((), () * 2))

in conclusion

Missing data processing is an important step in machine learning. sklearn provides a variety of tools to handle missing data, including deletion, fill, and prediction. The choice of the appropriate method depends on the characteristics of the data and the type of missing data. With proper processing, the performance and accuracy of the model can be improved.

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