Skip to main content

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 ...
Post a Comment

Mglearn

Image
Dr. Rishabh Pathak

mglearn is a utility Python library created specifically as a companion. It is designed to simplify the coding experience by providing helper functions for plotting, data loading, and illustrating machine learning concepts.

Purpose and Role of mglearn:

·         Illustrative Utility Library: mglearn includes functions that help visualize machine learning algorithms, datasets, and decision boundaries, which are especially useful for educational purposes and building intuition about how algorithms work.

·         Clean Code Examples: By using mglearn, the authors avoid cluttering the book’s example code with repetitive plotting or data preparation details, enabling readers to focus on core concepts without getting bogged down in boilerplate code.

·         Pre-packaged Example Datasets: It provides easy access to interesting datasets used throughout the book for demonstrating machine learning techniques, allowing readers to easily reproduce examples.

Common Uses of mglearn in the Book:

·         Plotting Functions: mglearn contains custom plotting functions that visualize classifiers, regression models, and clustering algorithms. For example, plotting decision boundary visuals for classifiers or graph representations of neural networks.

·         Data Visualization and Loading: It can generate synthetic datasets or load specific datasets with minimal code, speeding up prototyping and experimenting.

Practical Note from the Book:

While mglearn is a valuable teaching aid, and you may encounter it frequently within the book's code examples, it is not a required general-purpose library for machine learning. It is mainly geared toward demonstrating concepts in a clean and compact form, and knowing its functions is not critical for understanding or applying machine learning techniques.

Summary

mglearn is a specialized utility library bundled with Introduction to Machine Learning with Python to facilitate easy visualization, dataset loading, and clearer example code. It is a helpful pedagogical tool that complements the teaching of machine learning concepts but is not a general-purpose machine learning library

Python 2 vs Python 3

  1. Two Major Versions:
  • Python 2 (specifically 2.7) has been extensively used but is no longer actively developed.
  • Python 3 is the future of Python, with ongoing development and improvements. At the time of writing, Python 3.5 was the latest release mentioned.

2.      Compatibility Issues: Python 3 introduced major changes to the language syntax and standard libraries that make code written for Python 2 often incompatible with Python 3 without modifications. This can cause confusion when running or maintaining code written in one version on the other.

3.      Recommendation:

  • If starting a new project, or if you are learning Python now, the book strongly recommends using Python 3 because it represents the current and future ecosystem for Python programming,.
  • The book’s code has been written to be largely compatible with both Python 2 and 3, but some output differences might exist.

4.      Migration: For existing large codebases that still run on Python 2, immediate migration isn't required but should be planned as soon as feasible since Python 2 support is discontinued.

5.      Six Package (Migration Helper): The six package is mentioned as a helpful tool for writing code that runs on both Python 2 and Python 3. It abstracts differences and smooths out compatibility issues.

6.      Versions Used in the Book (Python 3 focus): The book uses Python 3 and specifies the versions of important libraries used for consistency (NumPy, pandas, matplotlib, etc.) to ensure reproducibility for readers.

Summary

  • Python 2 has been widely used but is now deprecated and no longer actively developed.
  • Python 3 introduced important changes and is the recommended version for all new machine learning projects.
  • Code compatibility issues exist, but tools like the six package can help write cross-compatible code.
  • The book’s code primarily supports Python 3 but is made to work under both versions with minor differences.
  • Users are advised to upgrade to Python 3 as soon as practical.

 

Categories:

Comments

Post a Comment

Popular posts from this blog

Relation of Model Complexity to Dataset Size

Dr. Rishabh Pathak
Core Concept The relationship between model complexity and dataset size is fundamental in supervised learning, affecting how well a model can learn and generalize. Model complexity refers to the capacity or flexibility of the model to fit a wide variety of functions. Dataset size refers to the number and diversity of training samples available for learning. Key Points 1. Larger Datasets Allow for More Complex Models When your dataset contains more varied data points , you can afford to use more complex models without overfitting. More data points mean more information and variety, enabling the model to learn detailed patterns without fitting noise. Quote from the book: "Relation of Model Complexity to Dataset Size. It’s important to note that model complexity is intimately tied to the variation of inputs contained in your training dataset: the larger variety of data points your dataset contains, the more complex a model you can use without overfitting....
Post a Comment
Read more

Linear Models

Dr. Rishabh Pathak
1. What are Linear Models? Linear models are a class of models that make predictions using a linear function of the input features. The prediction is computed as a weighted sum of the input features plus a bias term. They have been extensively studied over more than a century and remain widely used due to their simplicity, interpretability, and effectiveness in many scenarios. 2. Mathematical Formulation For regression , the general form of a linear model's prediction is: y^ ​ = w0 ​ x0 ​ + w1 ​ x1 ​ + … + wp ​ xp ​ + b where; y^ ​ is the predicted output, xi ​ is the i-th input feature, wi ​ is the learned weight coefficient for feature xi ​ , b is the intercept (bias term), p is the number of features. In vector form: y^ ​ = wTx + b where w = ( w0 ​ , w1 ​ , ... , wp ​ ) and x = ( x0 ​ , x1 ​ , ... , xp ​ ) . 3. Interpretation and Intuition The prediction is a linear combination of features — each feature contributes prop...
Post a Comment
Read more

Predicting Probabilities

Dr. Rishabh Pathak
1. What is Predicting Probabilities? The predict_proba method estimates the probability that a given input belongs to each class. It returns values in the range [0, 1] , representing the model's confidence as probabilities. The sum of predicted probabilities across all classes for a sample is always 1 (i.e., they form a valid probability distribution). 2. Output Shape of predict_proba For binary classification , the shape of the output is (n_samples, 2) : Column 0: Probability of the sample belonging to the negative class. Column 1: Probability of the sample belonging to the positive class. For multiclass classification , the shape is (n_samples, n_classes) , with each column corresponding to the probability of the sample belonging to that class. 3. Interpretation of predict_proba Output The probability reflects how confidently the model believes a data point belongs to each class. For example, in ...
Post a Comment
Read more

Kernelized Support Vector Machines

Dr. Rishabh Pathak
1. Introduction to SVMs Support Vector Machines (SVMs) are supervised learning algorithms primarily used for classification (and regression with SVR). They aim to find the optimal separating hyperplane that maximizes the margin between classes for linearly separable data. Basic (linear) SVMs operate in the original feature space, producing linear decision boundaries. 2. Limitations of Linear SVMs Linear SVMs have limited flexibility as their decision boundaries are hyperplanes. Many real-world problems require more complex, non-linear decision boundaries that linear SVM cannot provide. 3. Kernel Trick: Overcoming Non-linearity To allow non-linear decision boundaries, SVMs exploit the kernel trick . The kernel trick implicitly maps input data into a higher-dimensional feature space where linear separation might be possible, without explicitly performing the costly mapping . How the Kernel Trick Works: Instead of computing ...
Post a Comment
Read more

Supervised Learning

Dr. Rishabh Pathak
What is Supervised Learning? ·     Definition: Supervised learning involves training a model on a labeled dataset, where the input data (features) are paired with the correct output (labels). The model learns to map inputs to outputs and can predict labels for unseen input data. ·     Goal: To learn a function that generalizes well from training data to accurately predict labels for new data. ·          Types: ·          Classification: Predicting categorical labels (e.g., classifying iris flowers into species). ·          Regression: Predicting continuous values (e.g., predicting house prices). Key Concepts: ·     Generalization: The ability of a model to perform well on previously unseen data, not just the training data. ·         Overfitting and Underfitting: ·    ...
Post a Comment
Read more