- Machine Learning Basics
- Machine Learning - Home
- Machine Learning - Getting Started
- Machine Learning - Basic Concepts
- Machine Learning - Python Libraries
- Machine Learning - Applications
- Machine Learning - Life Cycle
- Machine Learning - Required Skills
- Machine Learning - Implementation
- Machine Learning - Challenges & Common Issues
- Machine Learning - Limitations
- Machine Learning - Reallife Examples
- Machine Learning - Data Structure
- Machine Learning - Mathematics
- Machine Learning - Artificial Intelligence
- Machine Learning - Neural Networks
- Machine Learning - Deep Learning
- Machine Learning - Getting Datasets
- Machine Learning - Categorical Data
- Machine Learning - Data Loading
- Machine Learning - Data Understanding
- Machine Learning - Data Preparation
- Machine Learning - Models
- Machine Learning - Supervised
- Machine Learning - Unsupervised
- Machine Learning - Semi-supervised
- Machine Learning - Reinforcement
- Machine Learning - Supervised vs. Unsupervised
- Machine Learning Data Visualization
- Machine Learning - Data Visualization
- Machine Learning - Histograms
- Machine Learning - Density Plots
- Machine Learning - Box and Whisker Plots
- Machine Learning - Correlation Matrix Plots
- Machine Learning - Scatter Matrix Plots
- Statistics for Machine Learning
- Machine Learning - Statistics
- Machine Learning - Mean, Median, Mode
- Machine Learning - Standard Deviation
- Machine Learning - Percentiles
- Machine Learning - Data Distribution
- Machine Learning - Skewness and Kurtosis
- Machine Learning - Bias and Variance
- Machine Learning - Hypothesis
- Regression Analysis In ML
- Machine Learning - Regression Analysis
- Machine Learning - Linear Regression
- Machine Learning - Simple Linear Regression
- Machine Learning - Multiple Linear Regression
- Machine Learning - Polynomial Regression
- Classification Algorithms In ML
- Machine Learning - Classification Algorithms
- Machine Learning - Logistic Regression
- Machine Learning - K-Nearest Neighbors (KNN)
- Machine Learning - Naïve Bayes Algorithm
- Machine Learning - Decision Tree Algorithm
- Machine Learning - Support Vector Machine
- Machine Learning - Random Forest
- Machine Learning - Confusion Matrix
- Machine Learning - Stochastic Gradient Descent
- Clustering Algorithms In ML
- Machine Learning - Clustering Algorithms
- Machine Learning - Centroid-Based Clustering
- Machine Learning - K-Means Clustering
- Machine Learning - K-Medoids Clustering
- Machine Learning - Mean-Shift Clustering
- Machine Learning - Hierarchical Clustering
- Machine Learning - Density-Based Clustering
- Machine Learning - DBSCAN Clustering
- Machine Learning - OPTICS Clustering
- Machine Learning - HDBSCAN Clustering
- Machine Learning - BIRCH Clustering
- Machine Learning - Affinity Propagation
- Machine Learning - Distribution-Based Clustering
- Machine Learning - Agglomerative Clustering
- Dimensionality Reduction In ML
- Machine Learning - Dimensionality Reduction
- Machine Learning - Feature Selection
- Machine Learning - Feature Extraction
- Machine Learning - Backward Elimination
- Machine Learning - Forward Feature Construction
- Machine Learning - High Correlation Filter
- Machine Learning - Low Variance Filter
- Machine Learning - Missing Values Ratio
- Machine Learning - Principal Component Analysis
- Machine Learning Miscellaneous
- Machine Learning - Performance Metrics
- Machine Learning - Automatic Workflows
- Machine Learning - Boost Model Performance
- Machine Learning - Gradient Boosting
- Machine Learning - Bootstrap Aggregation (Bagging)
- Machine Learning - Cross Validation
- Machine Learning - AUC-ROC Curve
- Machine Learning - Grid Search
- Machine Learning - Data Scaling
- Machine Learning - Train and Test
- Machine Learning - Association Rules
- Machine Learning - Apriori Algorithm
- Machine Learning - Gaussian Discriminant Analysis
- Machine Learning - Cost Function
- Machine Learning - Bayes Theorem
- Machine Learning - Precision and Recall
- Machine Learning - Adversarial
- Machine Learning - Stacking
- Machine Learning - Epoch
- Machine Learning - Perceptron
- Machine Learning - Regularization
- Machine Learning - Overfitting
- Machine Learning - P-value
- Machine Learning - Entropy
- Machine Learning - MLOps
- Machine Learning - Data Leakage
- Machine Learning - Resources
- Machine Learning - Quick Guide
- Machine Learning - Useful Resources
- Machine Learning - Discussion
Machine Learning - Mean, Median, Mode
Mean, Median, and Mode are statistical measures used to describe the central tendency of a dataset. In machine learning, these measures are used to understand the distribution of data and identify outliers. Here, we will explore the concepts of Mean, Median, and Mode and their implementation in Python.
Mean
The "mean" is the average value of a dataset. It is calculated by adding up all the values in the dataset and dividing by the number of observations. The mean is a useful measure of central tendency because it is sensitive to outliers, meaning that extreme values can significantly affect the value of the mean.
In Python, we can calculate the mean using the NumPy library, which provides a function called mean().
Median
The "median" is the middle value in a dataset. It is calculated by arranging the values in the dataset in order and finding the value that lies in the middle. If there are an even number of values in the dataset, the median is the average of the two middle values.
The median is a useful measure of central tendency because it is not affected by outliers, meaning that extreme values do not significantly affect the value of the median.
In Python, we can calculate the median using the NumPy library, which provides a function called median().
Mode
The "mode" is the most common value in a dataset. It is calculated by finding the value that occurs most frequently in the dataset. If there are multiple values that occur with the same frequency, the dataset is said to be bimodal, trimodal, or multimodal.
The mode is a useful measure of central tendency because it can identify the most common value in a dataset. However, it is not a good measure of central tendency for datasets with a wide range of values or datasets with no repeating values.
In Python, we can calculate the mode using the SciPy library, which provides a function called mode().
Python Implementation
Let's see an example of calculating mean, median, and mode for a salary table in Python using NumPy and Pandas −
import numpy as np
import pandas as pd
# create a sample salary table
salary = pd.DataFrame({
'employee_id': ['001', '002', '003', '004', '005', '006', '007',
'008', '009', '010'],
'salary': [50000, 65000, 55000, 45000, 70000, 60000, 55000, 45000,
80000, 70000]
})
# calculate mean
mean_salary = np.mean(salary['salary'])
print('Mean salary:', mean_salary)
# calculate median
median_salary = np.median(salary['salary'])
print('Median salary:', median_salary)
# calculate mode
mode_salary = salary['salary'].mode()[0]
print('Mode salary:', mode_salary)
Output
On executing this code, you will get the following output −
Mean salary: 59500.0 Median salary: 57500.0 Mode salary: 45000