Author: Tayyaba Syed

In machine learning, an epoch refers to a complete iteration over the entire training dataset during the model training process. In simpler terms, it is the number of times the algorithm goes through the entire dataset during the training phase. During the training process, the algorithm makes predictions on the training data, computes the loss,…

Stacking, also known as stacked generalization, is an ensemble learning technique in machine learning where multiple models are combined in a hierarchical manner to improve prediction accuracy. The technique involves training a set of base models on the original training dataset, and then using the predictions of these base models as inputs to a meta-model,…

Adversarial machine learning is a subfield of machine learning that focuses on studying the vulnerability of machine learning models to adversarial attacks. An adversarial attack is a deliberate attempt to fool a machine learning model by introducing small perturbations in the input data. These perturbations are often imperceptible to humans, but they can cause the…

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…