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Subsampling and Classifying with Scikit-learn
Subsampling and classifying are important techniques in machine learning, particularly for dealing with imbalanced datasets. In this article, we will explore how to use subsampling and classification techniques with the popular Python library Scikit-learn.
Subsampling
Subsampling is the process of randomly selecting a subset of data points from a larger dataset. This technique is often used when the dataset is imbalanced, meaning that one class of data significantly outweighs the other. Subsampling can help address this imbalance by ensuring that the training data contains an equal number of examples from each class.
In Scikit-learn, subsampling can be easily achieved using the sklearn.utils.resample function. This function allows you to specify the number of samples to select from each class, as well as the random seed for reproducibility.
Classifying with Scikit-learn
Once we have subsampled our data, we can move on to the classification step. Scikit-learn offers a wide range of classification algorithms, including popular ones like logistic regression, decision trees, support vector machines, and random forests.
To build a classification model in Scikit-learn, we simply need to instantiate the chosen algorithm, fit it to the training data, and then use it to make predictions on new data. The sklearn.model_selection.train_test_split function can be used to split our data into training and testing sets for validation.
Example
Let’s walk through a simple example of subsampling and classifying using Scikit-learn. In this example, we will use the famous Iris dataset, which contains samples of three different species of iris flowers.
“`python
from sklearn.datasets import load_iris
from sklearn.model_selection import train_test_split
from sklearn.utils import resample
from sklearn.linear_model import LogisticRegression
from sklearn.metrics import accuracy_score
# Load the Iris dataset
iris = load_iris()
X, y = iris.data, iris.target
# Subsample the data
X_subsampled, y_subsampled = resample(X, y, n_samples=50, random_state=42)
# Split the data into training and testing sets
X_train, X_test, y_train, y_test = train_test_split(X_subsampled, y_subsampled, test_size=0.2, random_state=42)
# Fit a logistic regression model
model = LogisticRegression()
model.fit(X_train, y_train)
# Make predictions
y_pred = model.predict(X_test)
# Calculate accuracy
accuracy = accuracy_score(y_test, y_pred)
print(f’Accuracy: {accuracy}’)
“`
In this example, we subsampled the Iris dataset to only include 50 samples and split it into training and testing sets. We then trained a logistic regression model on the training data and evaluated its performance on the testing data. Finally, we calculated the accuracy of the model on the testing set.
By combining subsampling with classification techniques in Scikit-learn, we can effectively deal with imbalanced datasets and build accurate machine learning models.
