/* Load libraries */ from sklearn.preprocessing import StandardScaler from sklearn.decomposition import TruncatedSVD from scipy.sparse import csr_matrix from sklearn import datasets import numpy as np
Load Digits Data And Make Sparse
/* Load the data */ digits = datasets.load_digits() /* Standardize the feature matrix */ X = StandardScaler().fit_transform(digits.data) /* Make sparse matrix */ X_sparse = csr_matrix(X)
Create Truncated Singular Value Decomposition
/* Create a TSVD */ tsvd = TruncatedSVD(n_components=10)
Run Truncated Singular Value Decomposition
/* Conduct TSVD on sparse matrix */ X_sparse_tsvd = tsvd.fit(X_sparse).transform(X_sparse)
/* Show results */ print('Original number of features:', X_sparse.shape) print('Reduced number of features:', X_sparse_tsvd.shape)
Original number of features: 64 Reduced number of features: 10
View Percent Of Variance Explained By New Features
/* Sum of first three components' explained variance ratios */ tsvd.explained_variance_ratio_[0:3].sum()
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.
Data Science Resources: Data Science Recipes and Applied Machine Learning Recipes
Introduction to Applied Machine Learning & Data Science for Beginners, Business Analysts, Students, Researchers and Freelancers with Python & R Codes @ Western Australian Center for Applied Machine Learning & Data Science (WACAMLDS) !!!
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