What is regression?

Regression is a supervised learning task that predicts a continuous output value.

What is regression?

Regression is supervised learning that predicts a continuous number: a house price, tomorrow's temperature, a product's sales count. The model learns a function from inputs to the output.

# square metres -> price (thousand TL)
X = [[60], [80], [100], [120]]
y = [1200, 1600, 1950, 2400]

The difference from classification is the output: classification asks "which group?", regression asks "how much?".

Regression models

In scikit-learn many regression models share the same interface; class names usually end in Regressor:

from sklearn.linear_model import LinearRegression
from sklearn.tree import DecisionTreeRegressor
from sklearn.neighbors import KNeighborsRegressor

for m in [LinearRegression(), DecisionTreeRegressor(), KNeighborsRegressor(2)]:
    m.fit(X, y)
    print(m.predict([[90]]))

The simplest and most interpretable is linear regression; we'll look at it in detail in this group.

Exercise First regression

Train a KNeighborsRegressor(n_neighbors=2) model and print the prediction for 90 m².

Show example solution

from sklearn.neighbors import KNeighborsRegressor

X = [[60], [80], [100], [120]]
y = [1200, 1600, 1950, 2400]

m = KNeighborsRegressor(n_neighbors=2)
m.fit(X, y)
print(m.predict([[90]]))

Test yourself

Which is a regression problem?
- Predicting a car's fuel consumption
- Whether an e-mail is spam
- The species of an animal in a photo
- Grouping customers
Show answer

Predicting a car's fuel consumption — Fuel consumption is a continuous number; the others are class or cluster questions.
Create the decision tree regression model.
```
m = DecisionTree____()
```
Show answer
```
m = DecisionTreeRegressor()
```
Match the outputs with the problem type.

Price: 1950

Regression
Class: spam

Classification
Cluster: 2

Clustering
Temperature: 21.4

Regression (continuous)

Next lesson: Linear regression

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