Generating Synthetic Data with Numpy and Scikit-Learn

Generating Synthetic Data with Numpy and Scikit-Learn

In this tutorial, we'll discuss the details of generating different synthetic datasets using Numpy and Scikit-learn libraries. We'll see how different samples can be generated from various distributions with known parameters.

Introduction

In this tutorial, we'll discuss the details of generating different synthetic datasets using Numpy and Scikit-learn libraries. We'll see how different samples can be generated from various distributions with known parameters.

We'll also discuss generating datasets for different purposes, such as regression, classification, and clustering. At the end we'll see how we can generate a dataset that mimics the distribution of an existing dataset.

The Need for Synthetic Data

In data science, synthetic data plays a very important role. It allows us to test a new algorithm under controlled conditions. In other words, we can generate data that tests a very specific property or behavior of our algorithm.

For example, we can test its performance on balanced vs. imbalanced datasets, or we can evaluate its performance under different noise levels. By doing this, we can establish a baseline of our algorithm's performance under various scenarios.

There are many other instances, where synthetic data may be needed. For example, real data may be hard or expensive to acquire, or it may have too few data-points. Another reason is privacy, where real data cannot be revealed to others.

Setting Up

Before we write code for synthetic data generation, let's import the required libraries:

import numpy as np

## Needed for plotting
import matplotlib.colors
import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import Axes3D

## Needed for generating classification, regression and clustering datasets
import sklearn.datasets as dt

## Needed for generating data from an existing dataset
from sklearn.neighbors import KernelDensity
from sklearn.model_selection import GridSearchCV

Then, we'll have some useful variables in the beginning:

## Define the seed so that results can be reproduced
seed = 11
rand_state = 11

## Define the color maps for plots
color_map = plt.cm.get_cmap('RdYlBu')
color_map_discrete = matplotlib.colors.LinearSegmentedColormap.from_list("", ["red","cyan","magenta","blue"])

python numpy scikit-learn

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