Data Science Basic | Classification Algorithms by Diljeet Singh | Learn Smarter
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Classification Algorithms

Classification techniques are essential for predicting labels or categories within datasets, utilizing algorithms such as Logistic Regression, Decision Trees, and K-Nearest Neighbors (KNN). These methods are critically evaluated using metrics like accuracy, precision, recall, and F1-score, alongside the confusion matrix to visualize prediction results. Proper selection of classifiers is vital based on the complexity of the problem, the data size, and the interpretability of the results.

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Sections

  • 1

    What Is Classification?

    Classification is a supervised learning method used to assign categories to data points.

    Learning Practice

  • 2

    Common Classification Algorithms

    This section introduces common classification algorithms used in supervised learning, including Logistic Regression, Decision Trees, and K-Nearest Neighbors (KNN).

    Learning Practice

  • 2.1

    Logistic Regression

    Logistic regression is a classification algorithm used for binary classification tasks.

    Learning Practice

  • 2.2

    Decision Tree

    Decision Trees are a significant supervised learning technique utilized in classification, offering a straightforward model of decisions based on feature splits.

    Learning Practice

  • 2.3

    K-Nearest Neighbors (Knn)

    K-Nearest Neighbors (KNN) is a classification algorithm that predicts the class of a data point based on the classes of its k nearest neighbors.

    Learning Practice

  • 3

    Train/test Split

    The Train/Test Split is a technique used in supervised learning to separate a dataset into training and testing subsets to evaluate the performance of classification algorithms.

    Learning Practice

  • 4

    Evaluating Classification Models

    This section focuses on model evaluation techniques for classification tasks, including the confusion matrix and key performance metrics.

    Learning Practice

  • 4.1

    Confusion Matrix

    The confusion matrix is a crucial tool for evaluating the performance of classification models, providing insight into the correctness of predictions.

    Learning Practice

  • 4.2

    Metrics

    This section explores evaluation metrics used to assess the performance of classification models, including the confusion matrix and various key metrics like accuracy, precision, recall, and F1-score.

    Learning Practice

  • 4.3

    Code

    This section introduces classification algorithms and outlines their applications in supervised learning.

    Learning Practice

  • 5

    Choosing The Right Classifier

    This section guides the reader in selecting appropriate classification algorithms based on data characteristics and problem types.

    Learning Practice

  • 6

    Visualizing Decision Boundaries (Optional For 2d Data)

    This section discusses how to visualize decision boundaries for classification algorithms using 2D data.

    Learning Practice

  • 7

    Chapter Summary

    This chapter introduces classification algorithms, covering the nature of classification and several key algorithms used for predictive modeling.

    Learning Practice

References

Chapter 11_ Classification Algorithms.pdf

Class Notes

Memorization

What we have learnt

  • Classification is a supervi...
  • Common classification algor...
  • Model evaluation techniques...

Final Test

Revision Tests