Dimensionality reduction: visualization techniques

Practicing Machine Learning Interview Questions in Python

Lisa Stuart

Data Scientist

Why dimensionality reduction?

Speed up ML training
Visualization
Improves accuracy

Visualization techniques

PCA
t-SNE

Visualizing with PCA

PCA plot

¹ https://districtdatalabs.silvrback.com/principal-component-analysis-with-python

Scree plot

¹ https://towardsdatascience.com/a-step-by-step-explanation-of-principal-component-analysis-b836fb9c97e2

t-SNE

Probabilistic
Pairs of data points
Low-dimensional embedding
Plot embeddings

Visualizing with t-SNE

# t-sne with loan data
from sklearn.manifold import TSNE
import seaborn as sns

loans =  pd.read_csv('loans_dataset.csv')

# Feature matrix
X = loans.drop('Loan Status', axis=1)

tsne = TSNE(n_components=2, verbose=1, perplexity=40)
tsne_results = tsne.fit_transform(X)

loans['t-SNE-PC-one'] = tsne_results[:,0]
loans['t-SNE-PC-two'] = tsne_results[:,1]


# t-sne viz
plt.figure(figsize=(16,10))
sns.scatterplot(
    x="t-SNE-PC-one", y="t-SNE-PC-two",
    hue="Loan Status",
    palette=sns.color_palette(["grey","blue"]),
    data=loans,
    legend="full",
    alpha=0.3
)

¹ https://scikit-learn.org/stable/modules/generated/sklearn.manifold.TSNE.html

Visualizing with t-SNE

t-sne plot

PCA vs t-SNE digits data

PCA and t-sne on digits dataset

¹ https://towardsdatascience.com/visualising-high-dimensional-datasets-using-pca-and-t-sne-in-python-8ef87e7915b

Let's practice!

Practicing Machine Learning Interview Questions in Python