Deep learning is a type of machine learning that uses neural networks with multiple layers, called deep neural networks, to analyze and understand complex data, such as images, speech, and text. In this essay, we will be discussing how to set up a deep learning model using Keras, a popular open-source library for …
SKLEARN Gradient Boosting Classifier with Monte Carlo Cross Validation Gradient Boosting Classifier is a machine learning technique used to classify items into different categories. It is an ensemble method that combines the predictions of multiple weak models, such as decision trees, to make a final prediction. The technique uses an iterative process where each …
IRIS Flower Classification using SKLEARN DecisionTree Classifier with Grid Search Cross Validation The IRIS flower is a popular example in the field of machine learning. It is a type of flower that has different variations, such as the setosa, virginica, and versicolor. In this blog, we will be discussing how to classify the …
Evaluate Machine Learning Algorithm – repeated kfold cross validation in R Evaluating the performance of a machine learning algorithm is an important step in understanding how well it will work on new, unseen data. One popular method for evaluating the performance of an algorithm is called “repeated k-fold cross validation.” In repeated k-fold cross validation, …
How to setup a Machine Learning Classification problem in R Setting up a machine learning classification problem in R involves several steps, such as loading the data, preparing the data, selecting and training a model, and evaluating the model’s performance. Loading the data: The first step is to load the data into R, which can …
How to classify “wine” using SKLEARN linear_models – Multiclass Classification in Python Classifying wine can be an important task for those in the wine industry. In machine learning, this task is known as multiclass classification, as there are multiple classes of wine that need to be identified. One popular approach to multiclass classification is to …
How to use SVM Classifier and Regressor in Python Support Vector Machine (SVM) can be used for both Classification and Regression tasks. In classification, the goal is to assign a label to a given input, like determining which digit the image represents (0-9) from an image of a handwritten digit. In Regression, the goal is …
How to use GradientBoosting Classifier and Regressor in Python Gradient Boosting is a powerful ensemble machine learning algorithm that creates multiple decision trees and combines their predictions to make more accurate predictions. It is used for both classification and regression problems. In this article, we will go over the basics of how to use Gradient …
How to use RandomForest Classifier and Regressor in Python Random Forest is an ensemble machine learning algorithm that creates multiple decision trees and combines their predictions to make more accurate predictions. It is used for both classification and regression problems. In this article, we will go over the basics of how to use Random Forest …
How to use AdaBoost Classifier and Regressor in Python AdaBoost is an ensemble machine learning algorithm that combines several weak models to create a strong model. It is used for both classification and regression problems, and it’s commonly used with decision tree models. In this article, we will go over the basics of how to …