Data Discretization is a process used in feature transformation to convert continuous data into categorical data.

It does so by dividing the range of the continuous data into a set of intervals.

Most machine learning algorithms are designed to work with categorical data. Discretization helps to make the continuous data more manageable by converting it to categorical data.

What is Data Discretization?

According to Wikipedia, “Data discretization, also known as quantization or binning, is the process of converting a continuous variable into a categorical or discrete variable by dividing the entire range of the variable into a set of intervals or bins.” In other words, data discretization involves grouping continuous data into a smaller number of discrete categories, making it easier to analyze and understand.

Why Learn Data Discretization?

Discretization is important for several reasons:

• Reduces the noise in continuous data and improves the accuracy of the machine learning model.
• Handles missing values better than continuous data.
• Handles irrelevant or redundant features better than continuous data.

Methods of Data Discretization

There are several methods for discretizing data, including:

1. Equal Width Binning
2. Equal Frequency Binning
3. K-Means Clustering
4. Decision Trees

Each method has its own advantages and disadvantages and the choice of method depends on the nature of the data and the requirements of the machine learning model.

Example of Data Discretization

Consider a dataset containing the heights of 100 individuals. The heights are continuous data and can range from 4 feet to 6 feet. To make this data easier to work with, we can discretize it into the following categories:

1. 4 to 4.5 feet
2. 4.5 to 5 feet
3. 5 to 5.5 feet
4. 5.5 to 6 feet

Each individual’s height can then be assigned to one of the above categories, making the data easier to work with and improving the accuracy of the machine learning model.

Python code example

Discretization of continuous data using pandas

import pandas as pd
import numpy as np

def discretize(df, column, bins):
    df[column] = pd.cut(df[column], bins=bins, labels=False)
    return df

df = pd.DataFrame({'col1': np.random.normal(0, 1, 100)})
bins = [-np.inf, -0.5, 0.5, np.inf]
df = discretize(df, 'col1', bins)

Python Code Examples:

Discretization Example:

import numpy as np
from sklearn.preprocessing import KBinsDiscretizer

data = np.array([[1.2], [2.4], [3.6], [4.8], [6.0]])

est = KBinsDiscretizer(n_bins=3, encode='ordinal', strategy='uniform')
est.fit(data)

transformed_data = est.transform(data)

print(transformed_data)

For more examples and information on Discretization in Python, check out this Stack Overflow post.

Useful Python Libraries for Discretization:

• NumPy: numpy.histogram
• Pandas: pandas.cut, pandas.qcut
• SciPy: scipy.stats.binned_statistic
• Scikit-learn: sklearn.preprocessing.KBinsDiscretizer, sklearn.tree.DecisionTreeClassifier, sklearn.tree.DecisionTreeRegressor

Datasets useful for Discretization:

UCI Machine Learning Repository – Wine Quality Data Set

# Python example
import pandas as pd
url = 'https://archive.ics.uci.edu/ml/machine-learning-databases/wine-quality/winequality-red.csv'
data = pd.read_csv(url, delimiter=';')
data.head()

UCI Machine Learning Repository – Abalone Data Set

# Python example
import pandas as pd
url = 'https://archive.ics.uci.edu/ml/machine-learning-databases/abalone/abalone.data'
names = ['sex', 'length', 'diameter', 'height', 'whole_weight', 'shucked_weight', 'viscera_weight', 'shell_weight', 'rings']
data = pd.read_csv(url, names=names)
data.head()

Example Data Discretization Visualization

Using Seaborn and Pandas, let’s visualize data discretization with the Iris dataset.

import seaborn as sns
import pandas as pd

# Load the iris dataset from Seaborn
df = sns.load_dataset('iris')

# Discretize the 'petal_width' column using Pandas
df['Discretized'] = pd.cut(df['petal_width'], bins=3, labels=['small', 'medium', 'large'])

# Plot the data
sns.countplot(x='Discretized', hue='species', data=df)

This code loads the iris dataset from Seaborn, discretizes the ‘petal_width’ column into three categories using the pd.cut function from Pandas, and then visualizes the distribution of the discretized data with respect to the ‘species’ column using the sns.countplot function from Seaborn. The resulting plot shows the number of flowers in each category of the discretized column, grouped by species. Note that this is just an example and the code may need to be adapted to fit different use cases.

Important Knowledge to Have to Better Understand Discretization:

• Understanding of basic statistics concepts such as mean, median, and standard deviation
• Knowledge of different types of data such as continuous, categorical, and ordinal
• Familiarity with feature engineering techniques
• Understanding of decision trees and entropy-based methods
• Knowledge of data preprocessing techniques such as normalization and scaling
• Experience with Python programming language and related libraries such as NumPy, Pandas, and Scikit-learn

Important Concepts in Discretization:

• Binning
• Entropy-based methods
• Decision tree-based methods
• Quantization
• Normalization
• Feature engineering
• Supervised and unsupervised Discretization methods
• Discretization evaluation metrics
• Discretization challenges and limitations

What’s Next?

After learning about Discretization in machine learning:

• Feature selection and dimensionality reduction techniques
• Clustering algorithms
• Supervised learning algorithms such as regression and classification
• Ensemble methods such as bagging, boosting, and stacking
• Deep learning and neural networks
• Time series analysis
• Natural language processing (NLP)

Relevant entities

Conclusion

Data Discretization is an important technique in the pre-processing stage of machine learning. It helps to convert continuous data into categorical data, making it easier to work with and improving the accuracy of the machine learning model. There are several methods for discretizing data, each with its own advantages and disadvantages, and the choice of method depends on the nature of the data and the requirements of the machine learning model.

What You May Want to Know About Discretization

sources:

• discretization-transforms-for-machine-learning/">Discretization Transforms for Machine Learning by Jason Brownlee
• Discretisation Techniques for Feature Engineering by Daniel Osei
• discretization-techniques-in-machine-learning-with-python-code/">Discretization Techniques in Machine Learning with Python Code by Diksha Singh
• discretization-in-machine-learning/">Discretization in Machine Learning by GeeksforGeeks
• Discretization of Continuous Features on Wikipedia