Introduction In the quest to understand relationships between variables, two terms consistently surface correlation and causation. Despite their apparent similarity, they have different implications and uses. This distinction is more than just a technicality; it’s a fundamental concept that every data analyst or scientist needs to grasp. The Basics of…
Feature scaling is an important technique in Machine Learning and it is one of the most important steps during the preprocessing of data before creating a machine learning model. The reason to perform features scaling is to ensure one feature doesn’t dominate others. The two most important scaling techniques are…
Cross-validation is a resampling procedure used in machine learning to evaluate a model’s performance when the underlying data sample is limited. It involves partitioning the original training dataset into a set of ‘k’ subsets (or “folds”), training the model on a ‘k-1’ subsets, and validating the model on the remaining…
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…
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…
Principal Component Analysis (PCA) is a dimensionality reduction technique commonly used in data analysis and machine learning. One of its primary objectives is to capture the most variance in the data while reducing the dimensionality of the dataset. Variance is a statistical measure that quantifies the spread or dispersion of…