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How to Create simulated data for classification in Python?
How to Create simulated data for classification in Python?
This recipe helps you Create simulated data for classification in Python
Recipe Objective
Do you ever wanted to generate dataset from python itself for any use. We can generate different types of data for different purposes from python.
So this recipe is a short example of how we can Create simulated data for classification in Python.
Step 1 - Import the library - GridSearchCv
from sklearn.datasets import make_classification
import pandas as pd
Here we have imported modules pandas and make_classification from differnt libraries. We will understand the use of these later while using it in the in the code snipet.
For now just have a look on these imports.
Step 2 - Generating the data
Here we are using make_classification to generate a classification data. We have stored features and targets.
- n_samples: It signifies the number of samples(row) we want in our dataset. By default it is set to 100
- n_features: It signifies the number of features(columns) we want in our dataset. By default it is set to 20
- n_informative: It is used to set the number of informative class. By default it is set to 2
- n_redundant : It is used to set number of redundant features. The features which can be generated as random linear combinations of the informative features. By default it is set to 2
- n_classes : This signifies the number of classes in target dataset.
features, output = make_classification(n_samples = 50,
n_features = 5,
n_informative = 5,
n_redundant = 0,
n_classes = 3,
weights = [.2, .3, .8])
Step 3 - Viewing the dataset
We are viewing first 5 observation of the features.
print("Feature Matrix: ");
print(pd.DataFrame(features, columns=["Feature 1", "Feature 2", "Feature 3", "Feature 4", "Feature 5"]).head())
We are viewing the first 5 observation of target.
print()
print("Target Class: ");
print(pd.DataFrame(output, columns=["TargetClass"]).head())
So the output comes as:
Feature Matrix: Feature 1 Feature 2 Feature 3 Feature 4 Feature 5 0 0.833135 -1.107635 -0.728420 0.101483 1.793259 1 1.120892 -1.856847 -2.490347 1.247622 1.594469 2 -0.980409 -3.042990 -0.482548 4.075172 -1.058840 3 0.827502 2.839329 2.943324 -2.449732 0.303014 4 1.173058 -0.519413 1.240518 -2.643039 2.406873 Target Class: TargetClass 0 2 1 2 2 1 3 0 4 2
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