Category: Machine Learning Miscellaneous

Data leakage is a common problem in machine learning that occurs when information from outside the training dataset is used to create or evaluate a model. This can lead to overfitting, where the model is too closely tailored to the training data and performs poorly on new data. There are two main types of data…

MLOps (Machine Learning Operations) is a set of practices and tools that combine software engineering, data science, and operations to enable the automated deployment, monitoring, and management of machine learning models in production environments. MLOps addresses the challenges of managing and scaling machine learning models in production, which include version control, reproducibility, model deployment, monitoring,…

Entropy is a concept that originates from thermodynamics and was later applied in various fields, including information theory, statistics, and machine learning. In machine learning, entropy is used as a measure of the impurity or randomness of a set of data. Specifically, entropy is used in decision tree algorithms to decide how to split the…

In machine learning, we use P-value to test the null hypothesis that there is no significant relationship between two variables. For example, if we have a dataset of house prices and we want to determine whether there is a significant relationship between the size of the house and its price, we can use P-value to…

Overfitting occurs when a model learns the noise in the training data, rather than the underlying patterns. This causes the model to perform well on the training data, but poorly on new data. Essentially, the model becomes too specialized to the training data, and is unable to generalize to new data. Overfitting is a common…

In machine learning, regularization is a technique used to prevent overfitting, which occurs when a model is too complex and fits the training data too well, but fails to generalize to new, unseen data. Regularization introduces a penalty term to the cost function, which encourages the model to have smaller weights and a simpler structure,…

Perceptron is one of the oldest and simplest neural network architectures. It was invented in the 1950s by Frank Rosenblatt. The Perceptron algorithm is a linear classifier that classifies input into one of two possible output categories. It is a type of supervised learning that trains the model by providing labeled training data. The Perceptron…

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…