The curse of dimensionality

Dimensionality Reduction in Python

Jeroen Boeye

Head of Machine Learning, Faktion

From observation to pattern

City Price
Berlin 2
Paris 3
Dimensionality Reduction in Python

From observation to pattern

City Price
Berlin 2
Paris 3

dull distribution plot

Dimensionality Reduction in Python

From observation to pattern

City Price
Berlin 2.0
Berlin 3.1
Berlin 4.3
Paris 3.0
Paris 5.2
... ...

Berlin vs. Paris distribution

Dimensionality Reduction in Python

Building a city classifier - data split

Separate the feature we want to predict from the ones to train the model on.

y = house_df['City']

X = house_df.drop('City', axis=1)

Perform a 70% train and 30% test data split

from sklearn.model_selection import train_test_split

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3)
Dimensionality Reduction in Python

Building a city classifier - model fit

Create a Support Vector Machine Classifier and fit to training data

from sklearn.svm import SVC

svc = SVC()

svc.fit(X_train, y_train)
Dimensionality Reduction in Python

Building a city classifier - predict

from sklearn.metrics import accuracy_score

print(accuracy_score(y_test, svc.predict(X_test)))

0.826

print(accuracy_score(y_train, svc.predict(X_train)))

0.832
Dimensionality Reduction in Python

Adding features

City Price
Berlin 2.0
Berlin 3.1
Berlin 4.3
Paris 3.0
Paris 5.2
... ...

Berlin vs. Paris distribution

Dimensionality Reduction in Python

Adding features

City Price n_floors n_bathroom surface_m2
Berlin 2.0 1 1 190
Berlin 3.1 2 1 187
Berlin 4.3 2 2 240
Paris 3.0 2 1 170
Paris 5.2 2 2 290
... ... ... ... ...
Dimensionality Reduction in Python

Let's practice!

Dimensionality Reduction in Python

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