Supervised and unsupervised

Supervised learning trains models with labeled data; each example comes with a correct output (label).

Supervised learning

In supervised learning the correct answer (label) of every example is known. The model learns the mapping from input to label.

If the label is a number, it is regression: price, temperature.
If the label is a class, it is classification: spam/normal, cat/dog.

X = [[1], [2], [3], [4]]   # inputs
y = [0, 0, 1, 1]           # labels (classes)
from sklearn.neighbors import KNeighborsClassifier
KNeighborsClassifier(n_neighbors=1).fit(X, y)

Unsupervised learning

In unsupervised learning there are no labels; the algorithm finds the structure of the data itself.

Clustering: groups similar customers (KMeans).
Dimensionality reduction: summarises many-column data with few columns (PCA).

from sklearn.cluster import KMeans
X = [[1, 1], [1, 2], [9, 9], [9, 8]]
KMeans(n_clusters=2, n_init=10).fit(X)   # no y given

The difference also shows in the fit call: unsupervised models get only X.

Exercise Train a classifier

Train a KNeighborsClassifier(n_neighbors=1) model with X and y, and print the class of the input [[3.5]].

Show example solution

from sklearn.neighbors import KNeighborsClassifier

X = [[1], [2], [3], [4]]
y = [0, 0, 1, 1]

model = KNeighborsClassifier(n_neighbors=1)
model.fit(X, y)
print(model.predict([[3.5]])[0])

Test yourself

Predicting a house price from its square metres is which kind of problem?
- Dimensionality reduction
- Supervised regression
- Supervised classification
- Clustering
Show answer

Supervised regression — The label (price) is known and is a continuous number, so it is supervised regression.

Train the unsupervised model with the inputs only.

km = KMeans(n_clusters=2, n_init=10)
km.fit(____)

Show answer

km = KMeans(n_clusters=2, n_init=10)
km.fit(X)

Match the problems with their types.

Spam detection

Classification
Temperature forecast

Regression
Customer grouping

Clustering
Reducing the number of columns

Dimensionality reduction

Next lesson: Data and model

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