Category: Machine Learning Miscellaneous

Precision and recall are two important metrics used to evaluate the performance of classification models in machine learning. They are particularly useful for imbalanced datasets where one class has significantly fewer instances than the other. Precision is a measure of how many of the positive predictions made by a classifier were correct. It is defined…

Bayes Theorem is a fundamental concept in probability theory that has many applications in machine learning. It allows us to update our beliefs about the probability of an event given new evidence. Actually, it forms the basis for probabilistic reasoning and decision making. Bayes Theorem states that the probability of an event A given evidence…

In machine learning, a cost function is a measure of how well a machine learning model is performing. It is a mathematical function that takes in the model’s predicted values and the true values of the data and outputs a single scalar value that represents the cost or error of the model’s predictions. The goal…

Gaussian Discriminant Analysis (GDA) is a statistical algorithm used in machine learning for classification tasks. It is a generative model that models the distribution of each class using a Gaussian distribution, and it is also known as the Gaussian Naive Bayes classifier. The basic idea behind GDA is to model the distribution of each class…

Apriori is a popular algorithm used for association rule mining in machine learning. It is used to find frequent itemsets in a transaction database and generate association rules based on those itemsets. The algorithm was first introduced by Rakesh Agrawal and Ramakrishnan Srikant in 1994. The Apriori algorithm works by iteratively scanning the database to…

Association rule mining is a technique used in machine learning to discover interesting patterns in large datasets. These patterns are expressed in the form of association rules, which represent relationships between different items or attributes in the dataset. The most common application of association rule mining is in market basket analysis, where the goal is…

In machine learning, the train-test split is a common technique used to evaluate the performance of a machine learning model. The basic idea behind the train-test split is to split the available data into two sets: a training set and a testing set. The training set is used to train the model, and the testing…

Data scaling is a pre-processing technique used in Machine Learning to normalize or standardize the range or distribution of features in the data. Data scaling is essential because the different features in the data may have different scales, and some algorithms may not work well with such data. By scaling the data, we can ensure…

Grid Search is a hyperparameter tuning technique in Machine Learning that helps to find the best combination of hyperparameters for a given model. It works by defining a grid of hyperparameters and then training the model with all the possible combinations of hyperparameters to find the best performing set. In other words, Grid Search is…

The AUC-ROC curve is a commonly used performance metric in machine learning that is used to evaluate the performance of binary classification models. It is a plot of the true positive rate (TPR) against the false positive rate (FPR) at different threshold values. What is the AUC-ROC Curve? The AUC-ROC curve is a graphical representation…