Hyperparameter Tuning is the process of systematically searching for the best combination of hyperparameters—the external configurations that control how a machine learning model learns. Unlike model parameters (which are learned during training), hyperparameters are set before training begins. As a...

Underfitting occurs when a machine learning model is too simplistic to capture the underlying patterns in the data, resulting in poor performance on both training and test sets. It fails to learn the signal from the data, often due to...

Overfitting is a common problem in machine learning where a model learns the training data too well, including its noise and outliers, resulting in poor generalization to new, unseen data. An overfitted model performs well on training data but fails...

Bias in AI refers to systematic errors in machine learning models that result in unfair, inaccurate, or discriminatory outcomes, often reflecting or amplifying existing societal biases present in the data. Bias can affect both the training process and the behavior...

Model interpretability refers to the ability to understand and explain how an AI or machine learning model arrives at its decisions.It is crucial for building trust, especially in high-stakes fields like healthcare, finance, and law. Interpretable models help users validate...

Explainable AI refers to a set of techniques and methods designed to make the decision-making processes of machine learning models transparent and understandable to humans. These methods are essential in applications where trust, fairness, and accountability are required, such as...

A White-Box Model is a type of machine learning model whose internal logic is fully transparent, interpretable, and understandable by humans. Unlike black-box models, white-box models allow users to trace exactly how input features influence predictions, making them ideal for...

A Black-Box Model refers to a machine learning or AI model whose internal workings are not easily understandable or interpretable by humans. These models often produce highly accurate results, but it’s difficult to explain how or why they arrived at...

Model Interpretability refers to the degree to which a human can understand the reasoning behind a model’s decisions or predictions. It plays a crucial role in evaluating whether an AI model behaves reliably, fairly, and transparently—especially in high-stakes or regulated...