Sklearn Linear
Sklearn Linear:
Scikit-learn (often referred to as sklearn) is a highly popular machine learning library in Python. It offers a simple and efficient set of tools for data mining and data analysis.
Its main features include:
- Classification algorithms such as SVM, nearest neighbors, random forest, and more.
- Regression algorithms like SVR, ridge regression, Lasso, etc.
- Clustering algorithms like k-means, spectral clustering, mean-shift, etc.
- Dimensionality reduction techniques like PCA, non-negative matrix factorization, etc.
- Model selection methods for cross-validation, grid search, etc.
- Preprocessing utilities for input scaling, feature extraction, etc.
Here’s an example of how you might use sklearn for a simple linear regression:
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LinearRegression
from sklearn import metrics
# Assume you have a dataset X and labels y
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=0)
regressor = LinearRegression()
regressor.fit(X_train, y_train) # training the algorithm
# To retrieve the intercept and slope:
print(regressor.intercept_) # For retrieving the intercept
print(regressor.coef_) # For retrieving the slope
# Predicting
y_pred = regressor.predict(X_test)
Remember to install the sklearn library using pip before using it:
pip install -U scikit-learnPython Training Demo Day 1
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