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Uncertainty in Multiclass Classification

Dr. Rishabh Pathak
1. What is Uncertainty in Classification? Uncertainty refers to the model’s confidence or doubt in its predictions. Quantifying uncertainty is important to understand how reliable each prediction is. In multiclass classification , uncertainty estimates provide probabilities over multiple classes, reflecting how sure the model is about each possible class. 2. Methods to Estimate Uncertainty in Multiclass Classification Most multiclass classifiers provide methods such as: predict_proba: Returns a probability distribution across all classes. decision_function: Returns scores or margins for each class (sometimes called raw or uncalibrated confidence scores). The probability distribution from predict_proba captures the uncertainty by assigning a probability to each class. 3. Shape and Interpretation of predict_proba in Multiclass Output shape: (n_samples, n_classes) Each row corresponds to the probabilities of ...
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Classification and Regression

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Dr. Rishabh Pathak


 Classification

Definition:

Classification is the supervised learning task of predicting a categorical class label from input data. Each example in the dataset belongs to one of a predefined set of classes.

Characteristics:

  • Outputs are discrete.
  • The goal is to assign each input to a single class.
  • Classes can be binary (two classes) or multiclass (more than two classes).

Examples:

  • Classifying emails as spam or not spam (binary classification).
  • Classifying iris flowers into one of three species (multiclass classification).

Types of Classification:

  • Binary Classification: Distinguishing between exactly two classes.
  • Multiclass Classification: Distinguishing among more than two classes.
  • Multilabel Classification: Assigning multiple class labels to each instance.

Key Concepts:

  • The class labels are discrete and come from a finite set.
  • Often expressed as a yes/no question in binary classification (e.g., “Is this email spam?”).
  • The predicted class labels are often encoded numerically but represent categories (e.g., 0, 1, 2 for iris species).

Regression

Definition:

Regression is the supervised learning task of predicting a continuous numerical value based on input features.

Characteristics:

  • Outputs are continuous and often real-valued numbers.
  • The model predicts a numeric quantity rather than a class.

Examples:

  • Predicting a person’s annual income from age, education, and location.
  • Predicting crop yield given weather and other factors.

Key Concepts:

  • Unlike classification, the output is a continuous value.
  • The task is about estimating the underlying function that maps inputs to continuous outputs.
  • Outputs can theoretically be any number within a range, reflecting real-world quantities.

Distinguishing Between Classification and Regression

An intuitive way to differentiate is based on the continuity of the output:

  • If the output is discrete (categorical classes), the problem is classification.
  • If the output is continuous (numerical values), the problem is regression.

Practical Examples and Representations:

  • The Iris dataset is a classic example for classification, with three species as classes.
  • For regression, datasets might involve predicting house prices, temperatures, or yields, with outputs as continuous numbers.
  • Input data can be numerical or categorical, but models require proper encoding and representation (e.g., one-hot encoding for categorical variables).

Summary and Usage

  • Classification and regression are foundational supervised learning tasks.
  • Choosing the right algorithm depends on the nature of the output (categorical vs continuous).
  • Preprocessing and feature representation are critical for both tasks to achieve good performance.
  • Many algorithms can be adapted for either task, but the interpretation and training differ accordingly.
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