Q&A 1 What is the goal of supervised learning?

1.1 Explanation

Supervised learning aims to learn a mapping from input features (X) to a target variable (y), based on labeled training data. The model can then make predictions on new, unseen data.

## Python Code
# Supervised learning example: simple linear regression
from sklearn.linear_model import LinearRegression
import pandas as pd

# Sample data
df = pd.DataFrame({
    "X": [1, 2, 3, 4, 5],
    "y": [2, 4, 6, 8, 10]
})

model = LinearRegression()
model.fit(df[["X"]], df["y"])
print("Model coefficient:", model.coef_[0])

Model coefficient: 2.0

1.2 R Code

# Supervised learning example: simple linear regression
df <- data.frame(X = 1:5, y = c(2, 4, 6, 8, 10))
model <- lm(y ~ X, data = df)
summary(model)

Call:
lm(formula = y ~ X, data = df)

Residuals:
         1          2          3          4          5 
 1.778e-16 -3.753e-16  1.314e-16  1.518e-16 -8.575e-17 

Coefficients:
             Estimate Std. Error   t value Pr(>|t|)    
(Intercept) 0.000e+00  2.841e-16 0.000e+00        1    
X           2.000e+00  8.565e-17 2.335e+16   <2e-16 ***
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 2.709e-16 on 3 degrees of freedom
Multiple R-squared:      1, Adjusted R-squared:      1 
F-statistic: 5.452e+32 on 1 and 3 DF,  p-value: < 2.2e-16