Machine Learning for Beginners in Python: Dimensionality Reduction With PCA

Dimensionality Reduction With PCA


/* Load libraries */
from sklearn.preprocessing import StandardScaler
from sklearn.decomposition import PCA
from sklearn import datasets

Load Data

/* Load the data */
digits = datasets.load_digits()

Standardize Feature Values

/* Standardize the feature matrix */
X = StandardScaler().fit_transform(

Conduct Principal Component Analysis

/* Create a PCA that will retain 99% of the variance */
pca = PCA(n_components=0.99, whiten=True)

/* Conduct PCA */
X_pca = pca.fit_transform(X)

View Results

/* Show results */
print('Original number of features:', X.shape[1])
print('Reduced number of features:', X_pca.shape[1])
/* outputs */
Original number of features: 64
Reduced number of features: 54


Python Example for Beginners

Two Machine Learning Fields

There are two sides to machine learning:

  • Practical Machine Learning:This is about querying databases, cleaning data, writing scripts to transform data and gluing algorithm and libraries together and writing custom code to squeeze reliable answers from data to satisfy difficult and ill defined questions. It’s the mess of reality.
  • Theoretical Machine Learning: This is about math and abstraction and idealized scenarios and limits and beauty and informing what is possible. It is a whole lot neater and cleaner and removed from the mess of reality.

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