Training a Simple Model
Let's build your first machine learning model: Linear Regression. It predicts a continuous value (e.g., price, temperature) based on input features. We'll use the diabetes dataset.
Step 1: Load Data and Split
from sklearn.datasets import load_diabetes
from sklearn.model_selection import train_test_split
diabetes = load_diabetes()
X_train, X_test, y_train, y_test = train_test_split(
diabetes.data, diabetes.target, test_size=0.2, random_state=42
)Step 2: Create and Train Model
from sklearn.linear_model import LinearRegression
model = LinearRegression()
model.fit(X_train, y_train) # learns patternsStep 3: Make Predictions
y_pred = model.predict(X_test)
print(y_pred[:5]) # first 5 predictionsWhat's Inside the Model?
print(model.coef_) # coefficients (slopes)
print(model.intercept_) # interceptHow It Works
Linear regression finds a line (or hyperplane) that minimizes the distance between predicted and actual values. The equation:
y = w1*x1 + w2*x2 + ... + b.Two Minute Drill
- LinearRegression is for predicting continuous values.
- Use
.fit(X_train, y_train)to train. - Use
.predict(X_test)to get predictions. - Model learns coefficients and intercept.
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