[Concept Entry] Sklearn: Linear Discriminant Analysis

content/sklearn/concepts/linear-discriminant-analysis/linear-discriminant-analysis.md

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+---
+Title: 'Linear Discriminant Analysis'
+Description: 'Linear Discriminant Analysis aims to project data onto a lower-dimensional space while preserving the information that discriminates between different classes.'
+Subjects:
+  - 'Data Science'
+  - 'Machine Learning'
+Tags:
+  - 'Machine Learning'
+  - 'Scikit-learn'
+  - 'Supervised Learning'
+  - 'Unsupervised Learning'
+CatalogContent:
+  - 'learn-python-3'
+  - 'paths/computer-science'
+---
+
+In Sklearn, **Linear Discriminant Analysis (LDA)** is a supervised algorithm that aims to project data onto a lower-dimensional space while preserving the information that discriminates between different classes. LDA finds a set of directions in the original feature space that maximize the separation between the classes. These directions are called discriminant directions. By projecting the data onto these directions, LDA reduces the dimensionality of the data while retaining the information that is most relevant for classification.
+
+## Syntax
+
+```pseudo
+from sklearn.discriminant_analysis import LinearDiscriminantAnalysis
+
+# Create an LDA model
+model = LinearDiscriminantAnalysis()
+
+# Fit the model to the training data
+model.fit(X_train, y_train)
+
+# Make predictions on the new data
+y_pred = model.predict(X_test)
+```
+
+## Example
+
+The following example demonstrates the implementation of LDA:
+
+```py
+from sklearn.discriminant_analysis import LinearDiscriminantAnalysis
+from sklearn.datasets import load_iris
+from sklearn.model_selection import train_test_split
+from sklearn.metrics import accuracy_score
+
+# Load the Iris dataset
+iris = load_iris()
+X = iris.data
+y = iris.target
+
+# Create training and testing sets by splitting the dataset
+X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=42)
+
+# Create an LDA model
+model = LinearDiscriminantAnalysis()
+
+# Fit the model to the training data
+model.fit(X_train, y_train)
+
+# Make predictions on the new data
+y_pred = model.predict(X_test)
+
+# Evaluate the model
+print("Accuracy:", accuracy_score(y_test, y_pred))
+```
+
+The above code produces the following output:
+
+```shell
+Accuracy: 1.0
+```
+
+## Codebyte Example
+
+The following codebyte example demonstrates the implementation of LDA:
+
+```codebyte/python
+from sklearn.discriminant_analysis import LinearDiscriminantAnalysis
+from sklearn.datasets import load_diabetes
+from sklearn.model_selection import train_test_split
+from sklearn.metrics import accuracy_score
+
+# Load the Diabetes dataset
+diabetes = load_diabetes()
+X = diabetes.data
+y = diabetes.target
+
+# Create training and testing sets by splitting the dataset
+X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.4, random_state=44)
+
+# Create an LDA model
+model = LinearDiscriminantAnalysis()
+
+# Fit the model to the training data
+model.fit(X_train, y_train)
+
+# Make predictions on the test set
+y_pred = model.predict(X_test)
+
+# Evaluate the model
+print("Accuracy:", accuracy_score(y_test, y_pred))
+```