- 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 - Box and Whisker Plots
A boxplot is a graphical representation of a dataset that displays the five-number summary of the data - the minimum value, the first quartile, the median, the third quartile, and the maximum value.
The boxplot consists of a box with whiskers extending from the top and bottom of the box.
The box represents the interquartile range (IQR) of the data, which is the range between the first and third quartiles.
The whiskers extend from the top and bottom of the box to the highest and lowest values that are within 1.5 times the IQR.
Any values that fall outside this range are considered outliers and are represented as points beyond the whiskers.
Python Implementation of Box and Whisker Plots
Now that we have a basic understanding of boxplots, let's implement them in Python. For our example, we will be using the Iris 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.
To start, we need to import the necessary libraries and load the dataset.
Example
import matplotlib.pyplot as plt import seaborn as sns from sklearn.datasets import load_iris iris = load_iris() data = iris.data target = iris.target
Next, we can create a boxplot of the sepal length for each of the three iris species using the Seaborn library.
plt.figure(figsize=(7.5, 3.5))
sns.boxplot(x=target, y=data[:, 0])
plt.xlabel('Species')
plt.ylabel('Sepal Length (cm)')
plt.show()
Output
This code will produce a boxplot of the sepal length for each of the three iris species, with the x-axis representing the species and the y-axis representing the sepal length in centimeters.
From this boxplot, we can see that the setosa species has a shorter sepal length compared to the versicolor and virginica species, which have a similar median and range of sepal lengths. Additionally, we can see that there are no outliers in the setosa species, but there are a few outliers in the versicolor and virginica specie.