Month: September 2023

Today, we made significant progress on your project. We dedicated our time to working on your project codebase and delving deeper into the concept of cross-validation.

And I also watched the videos of cross validation and I learnt the Cross-validation is a robust technique used to evaluate and validate the performance of machine learning models. It involves splitting your dataset into multiple subsets or “folds” and systematically training and testing your model on different combinations of these folds.

The day was marked by significant progress on your project, refining your code, and acquiring a deep understanding of the crucial concept of cross-validation.

In today’s class, we delved into the fascinating world of cross-validation and test error analysis applied to polynomial models using a dataset containing 354 data points related to diabetes.

There are 354 Data sets for which we  have records of all 3 variables (obesity, inactivity, diabetes) and I learnt about the K- flod  cross validation.

K-fold cross-validation is a widely used technique in machine learning for assessing the performance and generalization ability of a model, especially when you have a limited amount of data. The dataset is divided into K roughly equal-sized subsets or “folds. The standard choice for K is 5 or 10, but you can choose other values based on the size of your dataset and computational resources. and I also worked on my project and we are working on the given Datasets.

In today’s class, I learned about cross-validation, bootstrapping, and k-fold cross-validation.

K-Fold Cross-Validation:

K-fold cross-validation is a crucial technique in machine learning and data analysis. It involves dividing the original dataset into “k” subsets or folds, each of roughly equal size. The model is then trained and evaluated “k” times, with each fold taking turns as the validation set while the remaining folds serve as the training data. This comprehensive process ensures that every data point participates in both training and validation, leading to a more reliable assessment of model performance. By averaging the results from each iteration, K-fold cross-validation provides a robust estimate of how well a model generalizes to unseen data.

Bootstrapping: Bootstrapping is a resampling technique used for statistical inference. It involves creating multiple random samples (with replacement) from a given dataset.

And I watched the videos of the cross validation right and wrong ways. I learnt a little bit. Also I worked on my project.

Today I worked on my project. Initially, I embarked on this project with a basic understanding of machine learning and data analysis. However, as I progressed, I realized that simply training a model on a dataset and I learnt depth about the P-value and T-test.

I learnt about the Cross  validation and validation set approach. One crucial component of cross-validation is the validation set. This set is used to assess the model’s performance during training and fine-tuning. It acts as a sort of checkpoint, helping me adjust the model’s hyperparameters and detect any issues early in the development process. By separating a portion of my dataset for validation, I gained a clearer understanding of how well my model was learning from the data.

During today’s class, we delved into an intriguing aspect of  crab molt model. This area of study revolves around examining the patterns and behaviors associated with molting in crabs, shedding light on their growth and developmental processes.

Crab Molt Model: The crab molt model serves as a conceptual framework utilized by researchers to gain a deeper understanding of the molting process in crabs.

Premolt Data: Premolt data encompasses information and observations gathered from crabs in the period leading up to their molt. During this phase, crabs often undergo noticeable behavioral changes and physiological adjustments.

Postmolt Data: In contrast, postmolt data refers to information collected from crabs immediately after they have molted and are in the process of hardening their new exoskeleton.

T-Test: The T-test emerges as a valuable statistical tool frequently employed in scientific research to determine whether there exists a significant difference between two sets of data. In the context of the crab molt model, T-tests can be applied to assess whether there are statistically significant disparities among various parameters within premolt and postmolt data. and I also I am working on the project

Simple Linear Regression:

A dependent variable (Y) and an independent variable (X) are modelled using simple linear regression, a statistical technique. It presumes that there is a linear relationship between them, meaning that changes to X cause proportionate changes to Y. The objective is to identify the line that minimises the total of squared differences between the observed data points and the line’s projected values, typically denoted as Y = axe + b.

In a simple linear regression, ‘a’ stands for the slope of the line, which reflects how much Y changes for a one-unit change in X, and ‘b’ stands for the intercept, which represents the value of Y when X is zero.

Multiple Linear Regression

The extension of simple linear regression to incorporate many independent variables is known as multiple linear regression. It simulates the relationship between a number of independent variables (X1, X2, X3, etc.) and a dependent variable (Y). The formula for the equation is written as Y = a1X1 + a2X2 + a3X3 +… + b, where ‘a1’, ‘a2’, ‘a3’, etc., are the coefficients that indicate the influence of each independent variable on the dependent variable, and ‘b’ is the intercept.

We can examine the combined effects of numerous predictors on the response variable using multiple linear regression. It is commonly used to make predictions and comprehend complex relationships in a variety of disciplines, including economics, finance, and the social sciences.

Before you can use recent classes in your project, it’s essential to understand what these classes are and why they are relevant to your project. Recent classes typically refer to classes or components that have been recently developed. And I learnt depth about the P value and the heteroscedasticity for diabetes.

Certainly! The conversation regarding what a p-value is and how you participated in the MTH522 class are described below:

I went to my MTH522 class today, and the topic of discussion was the basic idea of p-values in statistics. The course was interesting and instructive, offering useful insights into the fields of statistical significance and hypothesis testing.

During the class, we delved into the concept of p-values, which are a vital component of statistical analysis. The instructor explained that a p-value is a numerical measure used to assess the strength of evidence against a null hypothesis. Essentially, it quantifies the likelihood of obtaining the observed results if the null hypothesis were indeed true. Overall, the MTH522 class provided a comprehensive and insightful exploration of p-values, equipping attendees like me with a better understanding of this critical statistical concept and its practical relevance in research and data analysis.

Today’s first MTH522 class delved into the world of statistics with a focus on simple linear regression using real-world data from the CDC’s diabetes graphs. We began by understanding the fundamentals of linear regression, a powerful statistical tool used to model relationships between variables.

And I listened to the topic that Residuals.Residuals are the differences between the observed values and the values predicted by our linear regression model.Recognizing and addressing heteroscedasticity .

In summary, today’s MTH522 class provided a comprehensive introduction to simple linear regression, using real data from the CDC’s diabetes graphs to illustrate key concepts.

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