- 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 - Skewness and Kurtosis
Skewness and kurtosis are two important measures of the shape of a probability distribution in machine learning.
Skewness refers to the degree of asymmetry of a distribution. A distribution is said to be skewed if it is not symmetrical about its mean. Skewness can be positive, indicating that the tail of the distribution is longer on the right-hand side, or negative, indicating that the tail of the distribution is longer on the left-hand side. A skewness of zero indicates that the distribution is perfectly symmetrical.
Kurtosis refers to the degree of peakedness of a distribution. A distribution with high kurtosis has a sharper peak and heavier tails than a normal distribution, while a distribution with low kurtosis has a flatter peak and lighter tails. Kurtosis can be positive, indicating a higher-than-normal peak, or negative, indicating a lower than normal peak. A kurtosis of zero indicates a normal distribution.
Both skewness and kurtosis can have important implications for machine learning algorithms, as they can affect the assumptions of the models and the accuracy of the predictions. For example, a highly skewed distribution may require data transformation or the use of non-parametric methods, while a highly kurtotic distribution may require different statistical models or more robust estimation methods.
Example
In Python, the SciPy library provides functions for calculating skewness and kurtosis of a dataset. For example, the following code calculates the skewness and kurtosis of a dataset using the skew() and kurtosis() functions −
import numpy as np
from scipy.stats import skew, kurtosis
# Generate a random dataset
data = np.random.normal(0, 1, 1000)
# Calculate the skewness and kurtosis of the dataset
skewness = skew(data)
kurtosis = kurtosis(data)
# Print the results
print('Skewness:', skewness)
print('Kurtosis:', kurtosis)
This code generates a random dataset of 1000 samples from a normal distribution with mean 0 and standard deviation 1. It then calculates the skewness and kurtosis of the dataset using the skew() and kurtosis() functions from the SciPy library. Finally, it prints the results to the console.
Output
On executing this code, you will get the following output −
Skewness: -0.04119418903611285 Kurtosis: -0.1152250196054534
The resulting skewness and kurtosis values should be close to zero for a normal distribution.