How to visualise Data in 2D density graph in R Visualizing data in a 2D density graph can be a useful way to understand the distribution of the data and identify patterns or outliers. A 2D density graph is a graph that shows the density of the data points in a two-dimensional space. In R, …
Month: May 2019
How to visualise Data in grey scale in R Visualizing data in gray scale can be useful in certain situations, such as when working with black and white printers or when you want to focus on the shape of the data rather than the color. In R, there are several ways to visualize data in …
How to visualise Data in multiple groups in R Visualizing data in multiple groups is a useful way to compare and contrast the characteristics of different groups of data. In R, there are several ways to visualize data in multiple groups, such as using side-by-side box plots, side-by-side bar plots, or small multiple plots. These …
How to utilize ggplot to visualise Data – scatter plots in R Visualizing data is an important step in understanding and interpreting the results of an analysis. One way to visualize data in R is by using the ggplot2 library, which is a powerful data visualization tool. One of the most common types of plots …
How to compare performance of different trained models in R Comparing the performance of different trained models is an important step in the model selection process. It allows you to evaluate how well each model is able to make predictions and to choose the best model for your problem. In R, there are several ways …
How to save trained model in R After training a model in R, it is often useful to save the model so that it can be used later without having to retrain it. R provides several ways to save a trained model, which include the save(), saveRDS(), and save() functions in the caret package. These …
How to utilise Confusion Matrix in R A confusion matrix is a table that is used to evaluate the performance of a classification model. It is used to compare the predicted values from a model with the actual values, and it helps to understand how well a model is doing at classifying data. In R, …
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How to do PCA in R to preprocess data Principal Component Analysis (PCA) is a technique used in data analysis to reduce the dimensionality of a dataset while retaining as much information as possible. In R, PCA can be used to preprocess data by transforming the original variables of a dataset into a new set …
How to preprocess data in R using Box-Cox Transformation Preprocessing data is an important step in any data analysis project. It involves cleaning and transforming the data so that it is ready for further analysis. One such transformation is the Box-Cox Transformation, which is used to normalize a dataset. In R, the Box-Cox Transformation can …