Category: Machine Learning
Dataset Splitting Techniques
For developing machine learning models, it is common to split the dataset into training and test subsets. The training set is used for fitting the model, that is, to estimate the hyperparameters […]
Adaptive Boosting Algorithm Explained
Adaptive Boosting or AdaBoost is one of the earliest implementations of the boosting algorithm. It forms the base of other boosting algorithms, like gradient boosting and XGBoost. In this post, we are […]
Data Splitting in Machine Learning
Dataset splitting is highly necessary to eliminate or reduce bias to training data in Machine Learning Models. This process is always done to prevent the machine learning algorithms from resulted into an […]
Confusion Matrix
When a Machine Learning model is built, various evaluation metrics are used to check the quality or the performance of a model. For classification models, metrics such as Accuracy, Confusion Matrix, Classification […]
Machine Learning Engineer
Machine learning is a fascinating branch of artificial intelligence that involves predicting and adapting outcomes as data is received. The demand for machine learning professionals has also grown exponentially in recent years. […]
Ensemble Learning in Machine Learning
Machine learning predictions follow following behavior. Models process given inputs and produce an output. The output is a prediction based on what pattern the models see during the training process. But in […]
Hybrid Machine Learning
Machine learning (ML) is a technique that allows complex systems with vast data to be learned, analyzed, and predicted. . Machine learning (ML) uses algorithms and statistical models to identify patterns, mine […]
Batch Machine Learning
In machine learning, batch learning also known as offline learning is a technique where the model is trained on the entire dataset available in certain time. Batch learning is suitable for scenarios […]
Online Machine Learning
Online Machine Learning is a method of machine learning in which data becomes available in a sequential order. It is used to update the best predictor for future data at each step, […]
Feature Encoding in Machine Learning
The real-world data needs processing before feeding it to a machine learning model. We know that around 80% of a data scientist’s time goes into data preprocessing and 20% of the time […]
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