- Machine Learning With Python
- Home
- Basics
- Python Ecosystem
- Methods for Machine Learning
- Data Loading for ML Projects
- Understanding Data with Statistics
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- Preparing Data
- Data Feature Selection
- ML Algorithms - Classification
- Introduction
- Logistic Regression
- Support Vector Machine (SVM)
- Decision Tree
- Naïve Bayes
- Random Forest
- ML Algorithms - Regression
- Random Forest
- Linear Regression
- ML Algorithms - Clustering
- Overview
- K-means Algorithm
- Mean Shift Algorithm
- Hierarchical Clustering
- ML Algorithms - KNN Algorithm
- Finding Nearest Neighbors
- Performance Metrics
- Automatic Workflows
- Improving Performance of ML Models
- Improving Performance of ML Model (Contd…)
- ML With Python - Resources
- Machine Learning With Python - Quick Guide
- Machine Learning with Python - Resources
- Machine Learning With Python - Discussion
L1 Normalization
It may be defined as the normalization technique that modifies the dataset values in a way that in each row the sum of the absolute values will always be up to 1. It is also called Least Absolute Deviations.
Example
In this example, we use L1 Normalize technique to normalize the data of Pima Indians Diabetes dataset which we used earlier. First, the CSV data will be loaded and then with the help of Normalizer class it will be normalized.
The first few lines of following script are same as we have written in previous chapters while loading CSV data.
from pandas import read_csv from numpy import set_printoptions from sklearn.preprocessing import Normalizer path = r'C:\pima-indians-diabetes.csv' names = ['preg', 'plas', 'pres', 'skin', 'test', 'mass', 'pedi', 'age', 'class'] dataframe = read_csv (path, names=names) array = dataframe.values
Now, we can use Normalizer class with L1 to normalize the data.
Data_normalizer = Normalizer(norm='l1').fit(array) Data_normalized = Data_normalizer.transform(array)
We can also summarize the data for output as per our choice. Here, we are setting the precision to 2 and showing the first 3 rows in the output.
set_printoptions(precision=2)
print ("\nNormalized data:\n", Data_normalized [0:3])
Output
Normalized data: [[0.02 0.43 0.21 0.1 0. 0.1 0. 0.14 0. ] [0. 0.36 0.28 0.12 0. 0.11 0. 0.13 0. ] [0.03 0.59 0.21 0. 0. 0.07 0. 0.1 0. ]]
