In machine learning, bias, and variance are two critical sources of errors in models. 1. Bias: Definition: Bias is the error due to overly simplistic assumptions in the learning algorithm. High bias can cause the algorithm to miss the relevant relations between features and target outputs (underfitting), thereby leading to…
Introduction In the context of data science and statistics, “good fit” refers to how well a statistical model describes the relationship between the input variables (features) and the output variable (target). A model with a good fit is one that captures the underlying structure of the data accurately without overcomplicating…
Underfitting refers to a model that cannot capture the underlying trend of the data. This happens when the model is too simple to handle the complexity of the data. Essentially, the model is a poor predictor both on the training dataset and on unseen or new data. Imagine you are…
Overfitting is a modeling error that occurs when a machine learning or statistical model is tailored too closely to the training dataset. In this scenario, the model performs well on the data it has been trained on but poorly on any new, unseen data. Essentially, the model learns the ‘noise’…
ROC curves and AUC are used to measure performance in machine earning. They are the most widely used evaluation metrics for checking any classification model’s performance. It tells how much the model is capable of distinguishing between classes. ROC (Receiver Operator Characteristic Curve) is a probability curve and AUC represents the…
Introduction The machine learning pipeline is a systematic and organized way to move through an ML project. Each step is essential and builds on the previous one, forming a path from understanding your problem to deploying a solution. Following this pipeline ensures a disciplined approach, which is vital for the…
Regularization is a process used to create an optimally complex model. A model should be as simple as possible. Ridge and Lasso regression are some of the simple techniques to reduce model complexity and prevent over-fitting which may result from simple linear regression. Linear regression is the simplest supervised machine learning…