- Machine Learning With Python
- Home
- Basics
- Python Ecosystem
- Methods for Machine Learning
- Data Loading for ML Projects
- Understanding Data with Statistics
- Understanding Data with Visualization
- 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
Machine Learning - Box and Whisker Plots
Box and Whisker plots, also called boxplots in short, is another useful technique to review the distribution of each attribute’s distribution. The following are the characteristics of this technique −
It is univariate in nature and summarizes the distribution of each attribute.
It draws a line for the middle value i.e. for median.
It draws a box around the 25% and 75%.
It also draws whiskers which will give us an idea about the spread of the data.
The dots outside the whiskers signifies the outlier values. Outlier values would be 1.5 times greater than the size of the spread of the middle data.
Example
In the following example, Python script will generate Density Plots for the distribution of attributes of Pima Indian Diabetes dataset.
from matplotlib import pyplot from pandas import read_csv path = r"C:\pima-indians-diabetes.csv" names = ['preg', 'plas', 'pres', 'skin', 'test', 'mass', 'pedi', 'age', 'class'] data = read_csv(path, names = names) data.plot(kind = 'box', subplots = True, layout = (3,3), sharex = False,sharey = False) pyplot.show()
Output
From the above plot of attribute’s distribution, it can be observed that age, test and skin appear skewed towards smaller values.
