How to visualise Dataset in R Visualizing a dataset in R is an important step in understanding the underlying patterns and relationships in the data. There are various data visualization techniques and packages available in R, such as ggplot2, lattice, and base R plotting functions. Some of the most commonly used visualizations in R include: …

# Month: February 2019

How to analyse a Dataset in R Analysing a dataset in R typically involves several steps, such as loading the data, cleaning and preparing the data, exploring the data, and building models. Loading the data: The first step is to load the data into R, which can be done using functions like read.csv() or read.table(). …

How to work with Control Flow in R Control flow is the process of controlling the order in which a computer program executes its instructions. In R, control flow is used to make decisions and control the flow of execution of a program based on certain conditions. There are several control flow statements in R, …

Variables and Data Frames in R In statistical analysis, a variable is a characteristic or value that can change or take on different values. For example, a person’s age, height, or weight are all variables. In computer programming, a variable is a container for a value that can change during the execution of a program. …

Linear Regression in R using Partial Least Squared Regression Linear regression is a statistical method that helps to understand the relationship between a dependent variable and one or more independent variables. It is represented by an equation in the form of Y = a + bX, where Y is the dependent variable, X is the …

Linear Regression in R using Principal Component Regression Linear regression is a statistical method that helps to understand the relationship between a dependent variable and one or more independent variables. It is represented by an equation in the form of Y = a + bX, where Y is the dependent variable, X is the independent …

Linear Regression in R using Stepwise Regression Linear Regression is a statistical method that helps to understand the relationship between a dependent variable and one or more independent variables. It is represented by an equation in the form of Y = a + bX, where Y is the dependent variable, X is the independent variable, …

Linear Regression in R using OLS Regression Linear regression is a statistical method that is used to predict a continuous outcome variable based on one or more predictor variables. In R, one way to perform linear regression is through OLS (Ordinary Least Squares) regression. The basic process for performing OLS regression in R is as …

How to find optimal parameters for CatBoost using GridSearchCV for Classification in Python To find the optimal parameters for CatBoost using GridSearchCV for Classification in Python, you can follow these steps: Define the CatBoostClassifier model and specify the range of parameter values you want to test. These can include parameters such as depth, learning rate, …

How to find optimal parameters for CatBoost using GridSearchCV for Regression in Python To find the optimal parameters for CatBoost using GridSearchCV for Regression in Python, you can follow these steps: Define the CatBoost model and specify the range of parameter values you want to test. These can include parameters such as depth, learning rate, …

How to find optimal parameters using RandomSearchCV in Regression in Python In machine learning, finding the best set of parameters for a model is an important step to achieve the best performance. One technique to find the optimal parameters is RandomizedSearchCV. RandomizedSearchCV is a method for parameter tuning in which random combinations of the parameters …

How to find optimal parameters using GridSearchCV in Regression in Python GridSearchCV is a method to find the best set of parameters for a machine learning model. It works by defining a range of parameters that you want to test and then evaluating the performance of the model for each combination of parameters. The goal …