What are the type of research?

Research can be classified into various types based on different criteria, including the purpose of the study, the nature of the research question, the methodology employed, and the scope of the investigation. Here are some common types of research:

1. Basic Research: Also known as pure or fundamental research, basic research aims to expand knowledge and understanding of fundamental principles and concepts without any immediate practical application. It focuses on theoretical exploration and the advancement of scientific knowledge.

2. Applied Research: Applied research is conducted to address specific practical problems, issues, or challenges and to generate solutions or interventions with direct relevance to real-world applications. It aims to solve practical problems and improve existing practices or processes.

3. Quantitative Research: Quantitative research involves the collection and analysis of numerical data to quantify relationships, patterns, and trends. It relies on statistical methods and measures to draw conclusions and make generalizations based on numerical data.

4. Qualitative Research: Qualitative research focuses on exploring and understanding complex phenomena, experiences, and perspectives through non-numerical data such as words, images, and observations. It emphasizes in-depth exploration, interpretation, and contextual understanding.

5. Mixed-Methods Research: Mixed-methods research combines both quantitative and qualitative approaches within a single study to provide a comprehensive understanding of a research problem. It involves collecting, analyzing, and integrating both numerical and non-numerical data.

6. Descriptive Research: Descriptive research aims to describe and portray the characteristics, features, and attributes of a particular individual, group, situation, or phenomenon. It provides a detailed account of the subject under study without manipulating variables.

7. Exploratory Research: Exploratory research is conducted to explore a new topic, phenomenon, or problem, generate initial insights, and formulate research questions. It aims to gain familiarity with a subject and identify potential research avenues.

8. Explanatory Research: Explanatory research seeks to explain the relationships between variables, identify causes and effects, and establish causal explanations for observed phenomena. It focuses on understanding the underlying mechanisms and processes.

9. Experimental Research: Experimental research involves manipulating one or more variables to observe the effects on another variable while controlling for extraneous factors. It aims to establish cause-and-effect relationships through controlled experiments.

10. Case Study Research: Case study research involves in-depth exploration and analysis of a specific case, individual, group, or organization to understand unique characteristics, contexts, and dynamics. It provides detailed insights into complex phenomena.

11. Action Research: Action research is a participatory approach where researchers collaborate with stakeholders to identify and address practical problems, implement interventions, and generate actionable knowledge for improving practices or processes.

These are some of the common types of research that researchers may employ based on the nature of their research questions, objectives, and methodologies. Each type of research offers unique strengths and limitations, and researchers select the most appropriate type based on their research goals and requirements.

Comments

Relation of Model Complexity to Dataset Size

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....

Linear Models

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...

Predicting Probabilities

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 ...

Supervised Learning

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: · ...

Kernelized Support Vector Machines

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 ...

Mashtishk Vigyan Anusandhan

Search This Blog