Citations issues
If you’ve added another author to your paper and posted it on arXiv, but Google Scholar doesn’t show the new author’s name in the citation, you can go to your paper listing in arxiv >>…
Kernel tricks, SVM properties & kernel choice
Some popular types of kernels in SVM: 1. Linear Kernel 2. Polynomial Kernel 3. Radial Basis Function (RBF) Kernel (Gaussian Kernel) 4. Sigmoid Kernel Visualizing the decision boundaries To visualize the decision boundaries, we’ll use…
Exponential distribution
This song helps us better remember the properties of the exponential distribution. The exponential distribution models time between events in a Poisson process, where occurrences are independent at a constant rate. Key features include its…
Phân ph?i m?
Phân ph?i m? (exponential distribution) là m?t phân ph?i xác su?t quan tr?ng trong lý thuy?t xác su?t và th?ng kê. Nó ???c s? d?ng ?? mô t? th?i gian gi?a các s? ki?n x?y…
Recover lost content in Overleaf via history
In Overleaf, the history feature allows you to track and revert changes made to your document. This is particularly useful if you’ve inadvertently deleted something important. Here’s a step-by-step guide on how to use the…
Logistic Regression: method + Python & R codes
Logistic regression & Bernoulli distribution Logistic regression is a statistical method used for analyzing datasets in which there are one or more independent variables that determine an outcome. The outcome is typically a binary variable,…
AIC and BIC for Feature Selection
Akaike Information Criterion (AIC) Bayesian Information Criterion (BIC) Comparison and Use in Feature Selection By applying AIC and BIC in feature selection, we can make informed decisions about which features to include in their models,…
KNN classification: practical notices & implementation using Python & R
We should normalize or standardize data before applying KNN because the algorithm is distance-based, and unscaled features can distort distance calculations, leading to biased results. In this example, we’ll use the Iris dataset, which is…
K-Nearest Neighbors (KNN): an introduction
K-Nearest Neighbors (KNN) is a popular algorithm used for both classification and regression tasks. In KNN, the output is a class membership, which is assigned based on the majority of the k nearest data points.…
Linear Discriminant Analysis Implementation in Python & R
Linear Discriminant Analysis (LDA) is a classifier that creates a linear decision boundary by fitting class-conditional densities to the data and applying Bayes’ rule. The model assumes that each class follows a Gaussian distribution with…
Stepwise Feature Selection +example
Stepwise feature selection is a systematic approach to identifying the most relevant features for a predictive model by combining both forward and backward selection techniques. The process begins with either an empty model. Then, we…
Backward feature selection + example
Backward feature selection involves iteratively removing the least significant feature from a model based on adjusted R-squared. In this example, we are predicting nuts collected by squirrels, features like temperature and rainfall are chosen as…
Forward feature selection: a step by step example
Forward feature selection starts with an empty model and adds features one by one. At each step, the feature that improves the model performance the most is added to the model. The process continues until…
ElasticNet Regression: Method & Codes
ElasticNet regression is a regularized regression method that linearly combines both L1 and L2 penalties of the Lasso and Ridge methods. This allows it to perform both feature selection (like Lasso) and maintain some of…
Ridge regression: method & R codes
Motivation Now, recall that for LASSO Ridge Regression: Ridge regression: Ridge adds the penalty, which is the sum of the squares of the coefficients, to the loss function in linear regression. Ridge regression shrinks the…
Lasso Regression and LassoCV: methods & Python codes
The Lasso (Least Absolute Shrinkage and Selection Operator) is a regression technique that enhances prediction accuracy and interpretability by applying L1 regularization to shrink coefficients. Unlike traditional regression methods, Lasso forces some coefficients to become…
Simple linear regression using train-test split in Python & R
An example of performing simple linear regression using train-test split where the process is as follows, 1. Generate a synthetic dataset: 2. Split the dataset: We use train_test_split to divide the data into training and…
How to Write a Proof in a paper
Reviewers are not required to read supplementary materials, but many do. Therefore, making your proof easy to read is important. General Guidelines: Example: Theorem 1: Let and be real numbers. If , then . The…
Example of using derivatives to find optimal drug dosage
Dosage Optimization: Pharmacologists use derivatives to find the optimal drug dosage that maximizes therapeutic effects while minimizing side effects. The concentration of the drug in the bloodstream is modeled as a function of time, and…
Example of using derivatives to optimize the material cost
Optimization of Material Usage: Engineers use derivatives to minimize the cost of materials while maintaining structural integrity. For example, determining the optimal dimensions of a container to minimize surface area for a given volume. Example:…
Examples of using the derivative tests for finding maximum/minimum
Problem 2: Find the minimum distance from the point (2, 3) to the line . Solution: More examples:
Combining datasets to increase sample size
Detailed information can be found in Combining datasets to improve model fitting or its presentation slide. Summary: The key points of the paper titled “Combining Datasets to Improve Model Fitting” are as follows: Problem and…
Expectation Maximization (EM) & implementation
Expectation Maximization (EM) is an iterative algorithm used for finding maximum likelihood estimates of parameters in statistical models, particularly when the model involves latent variables (variables that are not directly observed). The algorithm is commonly…
A comic guide to denoising noisy data
Handling noisy data is a crucial step in data preprocessing and analysis. In general, here are some common approaches to manage noisy data: 1. Data Cleaning 2. Data Transformation 3. Statistical Techniques 4. Machine Learning…
Writing the paragraph detailing the contribution of your paper
After you introduce the motivation of your work, it’s time to write a paragraph detailing the contribution of your work for a paper. In that paragraph, consider the following structure and key elements: Significance and…
A comical guide to Missing Not At Random (MNAR)
Recall that Missing Not At Random (MNAR) is a type of missing data mechanism where the probability of missingness is related to the unobserved data itself. Here are some more examples of MNAR: In each…
What’s Missing at Random (MAR)?
Missing at Random (MAR) is a statistical term indicating that the likelihood of data being missing is related to some of the observed data but not to the missing data itself. This means that the…
Tips & Tricks for research newbies
Coding and managing projects Using AI to better code, debug and manage projects How to export an R dataframe to LaTeX Paper writing: Analyze experiment results faster with ChatGPT How to write in Latex faster…
Check Plagiarism by grammarly
Plagiarism is the act of using someone else’s work, ideas, words, or intellectual property without proper acknowledgment or permission, and presenting it as your own. Even reusing your own previously published work or parts of…
Multiple regression analysis: waiting time to log in to Windows
Multiple regression analysis can be used to understand the relationship between the waiting time to log in to Windows (dependent variable) and several independent variables. Let’s assume we have the following independent variables: Suppose that…
Using AI to better code, debug and manage projects
1. Code Completion and Suggestions AI-powered code completion tools can predict and suggest the next lines of code based on the context of your current coding. Examples include: Copilot: Uses OpenAI’s Codex to provide code…
Analyze experiment results faster with ChatGPT
I have experiment and I want to put the best result in bold in Latex, but it takes time to compare values and identify the smallest values in each row. So, I can ask ChatGPT…