- ML - Home
- ML - Introduction
- ML - Getting Started
- ML - Basic Concepts
- ML - Ecosystem
- ML - Python Libraries
- ML - Applications
- ML - Life Cycle
- ML - Required Skills
- ML - Implementation
- ML - Challenges & Common Issues
- ML - Limitations
- ML - Reallife Examples
- ML - Data Structure
- ML - Mathematics
- ML - Artificial Intelligence
- ML - Neural Networks
- ML - Deep Learning
- ML - Getting Datasets
- ML - Categorical Data
- ML - Data Loading
- ML - Data Understanding
- ML - Data Preparation
- ML - Models
- ML - Supervised Learning
- ML - Unsupervised Learning
- ML - Semi-supervised Learning
- ML - Reinforcement Learning
- ML - Supervised vs. Unsupervised
- Machine Learning Data Visualization
- ML - Data Visualization
- ML - Histograms
- ML - Density Plots
- ML - Box and Whisker Plots
- ML - Correlation Matrix Plots
- ML - Scatter Matrix Plots
- Statistics for Machine Learning
- ML - Statistics
- ML - Mean, Median, Mode
- ML - Standard Deviation
- ML - Percentiles
- ML - Data Distribution
- ML - Skewness and Kurtosis
- ML - Bias and Variance
- ML - Hypothesis
- Regression Analysis In ML
- ML - Regression Analysis
- ML - Linear Regression
- ML - Simple Linear Regression
- ML - Multiple Linear Regression
- ML - Polynomial Regression
- Classification Algorithms In ML
- ML - Classification Algorithms
- ML - Logistic Regression
- ML - K-Nearest Neighbors (KNN)
- ML - Naïve Bayes Algorithm
- ML - Decision Tree Algorithm
- ML - Support Vector Machine
- ML - Random Forest
- ML - Confusion Matrix
- ML - Stochastic Gradient Descent
- Clustering Algorithms In ML
- ML - Clustering Algorithms
- ML - Centroid-Based Clustering
- ML - K-Means Clustering
- ML - K-Medoids Clustering
- ML - Mean-Shift Clustering
- ML - Hierarchical Clustering
- ML - Density-Based Clustering
- ML - DBSCAN Clustering
- ML - OPTICS Clustering
- ML - HDBSCAN Clustering
- ML - BIRCH Clustering
- ML - Affinity Propagation
- ML - Distribution-Based Clustering
- ML - Agglomerative Clustering
- Dimensionality Reduction In ML
- ML - Dimensionality Reduction
- ML - Feature Selection
- ML - Feature Extraction
- ML - Backward Elimination
- ML - Forward Feature Construction
- ML - High Correlation Filter
- ML - Low Variance Filter
- ML - Missing Values Ratio
- ML - Principal Component Analysis
- Reinforcement Learning
- ML - Reinforcement Learning Algorithms
- ML - Exploitation & Exploration
- ML - Q-Learning
- ML - REINFORCE Algorithm
- ML - SARSA Reinforcement Learning
- ML - Actor-critic Method
- ML - Monte Carlo Methods
- ML - Temporal Difference
- Deep Reinforcement Learning
- ML - Deep Reinforcement Learning
- ML - Deep Reinforcement Learning Algorithms
- ML - Deep Q-Networks
- ML - Deep Deterministic Policy Gradient
- ML - Trust Region Methods
- Quantum Machine Learning
- ML - Quantum Machine Learning
- ML - Quantum Machine Learning with Python
- Machine Learning Miscellaneous
- ML - Performance Metrics
- ML - Automatic Workflows
- ML - Boost Model Performance
- ML - Gradient Boosting
- ML - Bootstrap Aggregation (Bagging)
- ML - Cross Validation
- ML - AUC-ROC Curve
- ML - Grid Search
- ML - Data Scaling
- ML - Train and Test
- ML - Association Rules
- ML - Apriori Algorithm
- ML - Gaussian Discriminant Analysis
- ML - Cost Function
- ML - Bayes Theorem
- ML - Precision and Recall
- ML - Adversarial
- ML - Stacking
- ML - Epoch
- ML - Perceptron
- ML - Regularization
- ML - Overfitting
- ML - P-value
- ML - Entropy
- ML - MLOps
- ML - Data Leakage
- ML - Monetizing Machine Learning
- ML - Types of Data
- Machine Learning - Resources
- ML - Quick Guide
- ML - Cheatsheet
- ML - Interview Questions
- ML - Useful Resources
- ML - Discussion
Machine Learning - Correlation Matrix Plot
A correlation matrix plot is a graphical representation of the pairwise correlation between variables in a dataset. The plot consists of a matrix of scatterplots and correlation coefficients, where each scatterplot represents the relationship between two variables, and the correlation coefficient indicates the strength of the relationship. The diagonal of the matrix usually shows the distribution of each variable.
The correlation coefficient is a measure of the linear relationship between two variables and ranges from -1 to 1. A coefficient of 1 indicates a perfect positive correlation, where an increase in one variable is associated with an increase in the other variable. A coefficient of -1 indicates a perfect negative correlation, where an increase in one variable is associated with a decrease in the other variable. A coefficient of 0 indicates no correlation between the variables.
Python Implementation of Correlation Matrix Plots
Now that we have a basic understanding of correlation matrix plots, let's implement them in Python. For our example, we will be using the Iris flower dataset from Sklearn, which contains measurements of the sepal length, sepal width, petal length, and petal width of 150 iris flowers, belonging to three different species - Setosa, Versicolor, and Virginica.
Example
import numpy as np
import pandas as pd
import seaborn as sns
from sklearn.datasets import load_iris
iris = load_iris()
data = pd.DataFrame(iris.data, columns=iris.feature_names)
target = iris.target
plt.figure(figsize=(7.5, 3.5))
corr = data.corr()
sns.set(style='white')
mask = np.zeros_like(corr, dtype=np.bool)
mask[np.triu_indices_from(mask)] = True
f, ax = plt.subplots(figsize=(11, 9))
cmap = sns.diverging_palette(220, 10, as_cmap=True)
sns.heatmap(corr, mask=mask, cmap=cmap, vmax=.3, center=0,
square=True, linewidths=.5, cbar_kws={"shrink": .5})
plt.show()
Output
This code will produce a correlation matrix plot of the Iris dataset, with each square representing the correlation coefficient between two variables.
From this plot, we can see that the variables 'sepal width (cm)' and 'petal length (cm)' have a moderate negative correlation (-0.37), while the variables 'petal length (cm)' and 'petal width (cm)' have a strong positive correlation (0.96). We can also see that the variable 'sepal length (cm)' has a weak positive correlation (0.87) with the variable 'petal length (cm)'.