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

The Objective of the problem to be studied

Image
Dr. Rishabh Pathak

The objective of the problem to be studied in a research project is a critical aspect that guides the entire research process and shapes the research design, methodology, data collection, analysis, and interpretation. Here are key considerations related to defining the objective of the problem in research:


1.    Clarity and Specificity:

o    The research objective should be clearly defined and specific to ensure a focused and purposeful investigation. Clearly stating the research problem helps in identifying the scope, boundaries, and goals of the study.

2.    Research Questions:

o    The research objective often translates into specific research questions that guide the inquiry and exploration of the problem. Formulating precise research questions helps in structuring the study, identifying variables, and generating hypotheses.

3.    Purpose of the Study:

o    The objective of the research problem determines the purpose of the study, whether it is exploratory, descriptive, diagnostic, or hypothesis-testing in nature. Understanding the purpose helps in selecting appropriate research methods and techniques.

4.    Research Gap:

o    The research objective should address a gap in existing knowledge or literature, aiming to contribute new insights, theories, or evidence to the field of study. Identifying the research gap provides rationale and significance for the research endeavor.

5.    Research Scope:

o    The objective of the problem defines the scope and boundaries of the research study, indicating what will be included and excluded from the investigation. Clarifying the research scope helps in focusing the research efforts and resources effectively.

6.    Research Objectives:

o    The research objective may be broken down into specific research objectives or goals that outline the intended outcomes, deliverables, or achievements of the study. Setting clear research objectives guides the research process and evaluation of results.

7.    Alignment with Research Design:

o    The objective of the problem should align with the chosen research design, methodology, and approach. Different research objectives may require distinct research designs, such as qualitative, quantitative, mixed methods, experimental, or case study designs.

8.    Feasibility and Relevance:

o    The research objective should be feasible within the constraints of time, resources, and expertise available for the study. Ensuring the relevance and practicality of the research objective enhances the likelihood of achieving meaningful outcomes.

9.    Stakeholder Engagement:

o    Engaging stakeholders, collaborators, or beneficiaries in defining the research objective can ensure that the study addresses relevant issues, meets stakeholders' needs, and generates actionable insights or recommendations.

10. Measurable Outcomes:

o    The research objective should be formulated in a way that allows for the measurement of outcomes, results, or impact. Establishing measurable criteria or indicators helps in evaluating the success and effectiveness of the research study.

By carefully defining and articulating the objective of the problem to be studied, researchers can establish a clear direction, purpose, and framework for their research endeavors. A well-defined research objective guides the research process, shapes research decisions, and contributes to the generation of valuable knowledge and insights in the chosen field of study.

 

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

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
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