s-Permutation
Example: password generation. Suppose you are generating a password using the characters A, B, and C. The password must be 3 characters long, and each character can be used once. Here, the S-permutation will be… s-Permutation
Why do monkeys swing
Why do monkeys swing from branch to branch in the forest? The swinging behavior of monkeys is a crucial aspect of their survival and daily activities. Swinging allows them to efficiently navigate the dense canopy,… Why do monkeys swing
Polar bear long swim
Polar bears are exceptional swimmers, capable of navigating the frigid Arctic waters with remarkable endurance. Swimming from one iceberg to another can take them anywhere from one to almost ten days, depending on the distance… Polar bear long swim
9 brain octopus & gender changed clownfish
Octopuses possess an extraordinary neurological structure, having nine brains in total. They have one central brain that handles overall coordination and cognitive functions, and an additional mini-brain in each of their eight arms. This unique… 9 brain octopus & gender changed clownfish
Transparent polar bears
Here are some more fun facts about polar bears: These unique traits and behaviors enable polar bears to thrive in the harsh Arctic environment.
Quizzes about the product rule
Question 1: There are 4 different types of shirts and 3 different types of pants. How many different outfits can you make with one shirt and one pair of pants? A. 7B. 10C. 12D. 15… Quizzes about the product rule
Quizzes: Complementary rules of probability
More quizzes In a survey, 80% of respondents prefer Brand A over Brand B. If a respondent is selected at random, what is the probability that they prefer Brand B? In a quality control test,… Quizzes: Complementary rules of probability
Quizzes: Classical definition of probability
If you flip a fair coin, what is the probability that it will land on heads? In a class of 30 students, 18 are girls and 12 are boys. What is the probability that a… Quizzes: Classical definition of probability
Fixed: app crash after integrating Admob to Unity project
If your app crash after updating admob, see the final part. First, check if you input the correct AdMob/Ad Manager Application ID. The correct one should be in the format: “ca-app-pub-################~##########”. A sample AdMob Application… Fixed: app crash after integrating Admob to Unity project
Unity & Admob bugs fixed
List of popular bugs: Fixed: app crash after integrating Admob to Unity project Fixed: Unity Gradle Build failure Fixed: Could not resolve all files for configuration ‘:launcher:releaseRuntimeClasspath’. More debugging tips: Debugging in Unity can sometimes… Unity & Admob bugs fixed
Encoding categorical data in python
Handling categorical data involves several steps to convert it into a format that machine learning algorithms can process effectively. Here are common methods used to handle categorical data: 1. Label Encoding Label encoding converts categorical… Encoding categorical data in python
Fixed: Could not resolve all files for configuration ‘:launcher:releaseRuntimeClasspath’.
This is done on Unity 2022.3.28f1 and I encountered this issue when trying to export the app after installing Ads Mediation from Unity First, go to File >> Build Setting >> Player Settings >> Enable… Fixed: Could not resolve all files for configuration ‘:launcher:releaseRuntimeClasspath’.
example of stack generalization using K-Nearest Neighbors (KNN) and Random Forest + Python codes
This example demonstrates the basic steps of stack generalization with two classifiers (KNN and Random Forest) and a Logistic Regression model as the meta-learner. The predictions of the base models on the training data are… example of stack generalization using K-Nearest Neighbors (KNN) and Random Forest + Python codes
Fixed: Unity Gradle Build failure
Google has API requirement and if you already got the new SDK API installed to do that, but after that you encounter Gradle building issue, try this: Go to Player Settings >> Publishing Settings Check… Fixed: Unity Gradle Build failure
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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 >>… Citations issues
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… Kernel tricks, SVM properties & kernel choice
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 probability density and cumulative distribution functions, mean, variance, and memoryless property. It has applications in queueing theory, reliability engineering, and survival analysis.
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… Phân ph?i m?
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… Recover lost content in Overleaf via history
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,… Logistic Regression: method + Python & R codes
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,… AIC and BIC for Feature Selection
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… KNN classification: practical notices & implementation using Python & R
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.… K-Nearest Neighbors (KNN): an introduction
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… Linear Discriminant Analysis Implementation in Python & R
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… Stepwise Feature Selection +example
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 significant predictors through this method. The process aims to finalize a model with the most influential features.
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… Forward feature selection: a step by step example
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… ElasticNet Regression: Method & Codes
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… Ridge regression: method & R codes
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… Lasso Regression and LassoCV: methods & Python codes
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… Simple linear regression using train-test split in Python & R