Deep learning is a subset of machine learning. Deep learning is inspired by the human brain. Deep learning is based on artificial neural networks. The “deep” in deep learning refers to the number of layers through which the data has to go through before the output layer. Neural networks use a…
A support vector machine (SVM) is a supervised machine learning model which can be used for both classification and regression. But they have been extensively used for solving complex classification problems such as image recognition, voice detection etc. SVM algorithm outputs an optimal hyperplane that best separates the tags. The hyperplane is a boundary that…
A confusion matrix is a fundamental tool in the field of machine learning and data science, often used to assess the performance of classification models. It provides a detailed breakdown of the model’s predictions compared to the actual ground truth, allowing us to evaluate various aspects of model performance. The…
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’…