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
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
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
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’.
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
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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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
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
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? (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?
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 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, meaning it has two possible… Logistic Regression: method + Python & R codes
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
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) 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 (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 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 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 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 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
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
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
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
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… How to Write a Proof in a paper
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 find optimal drug dosage
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:… Example of using derivatives to optimize the material cost
Problem 2: Find the minimum distance from the point (2, 3) to the line . Solution: More examples:
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… Combining datasets to increase sample size
Supplementary contents for section “Calculus can hurt” in the KSML app: Limit: Derivatives Integral
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… Expectation Maximization (EM) & implementation
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… A comic guide to denoising noisy data