Brownian Motion
/ˈbraʊ.ni.ən ˈmoʊ.ʃən/
noun … “random jittering with a mathematical rhythm.”
Markov Process
/ˈmɑːr.kɒv ˈprəʊ.ses/
noun … “the future depends only on the present, not the past.”
Markov Process is a stochastic process in which the probability of transitioning to a future state depends solely on the current state, independent of the sequence of past states. This “memoryless” property, known as the Markov property, makes Markov Processes a fundamental tool for modeling sequential phenomena in probability, statistics, and machine learning, including Hidden Markov Models, reinforcement learning, and time-series analysis.
Naive Bayes
/naɪˈiːv ˈbeɪz/
noun … “probabilities, simplified and fast.”
Naive Bayes is a probabilistic machine learning algorithm based on Bayes’ theorem that assumes conditional independence between features given the class label. Despite this “naive” assumption, it performs remarkably well for classification tasks, particularly in text analysis, spam detection, sentiment analysis, and document categorization. The algorithm calculates the posterior probability of each class given the observed features and assigns the class with the highest probability.
Maximum Likelihood Estimation
/ˈmæksɪməm ˈlaɪk.li.hʊd ˌɛstɪˈmeɪʃən/
noun … “finding the parameters that make your data most believable.”
Singular Value Decomposition
/ˈsɪŋ.ɡjʊ.lər ˈvæl.ju dɪˌkɑːm.pəˈzɪʃ.ən/
noun … “disassembling a matrix into its hidden building blocks.”
Kernel Function
/ˈkɜːr.nəl ˈfʌŋk.ʃən/
noun … “measuring similarity in disguise.”
Kernel Trick
/ˈkɜːr.nəl trɪk/
noun … “mapping the invisible to the visible.”
Gradient Boosting
/ˈɡreɪ.di.ənt ˈbuː.stɪŋ/
noun … “learning from mistakes, one step at a time.”
Random Forest
/ˈrændəm fɔːrɪst/
noun … “many trees, one wise forest.”
Information Gain
/ˌɪn.fərˈmeɪ.ʃən ɡeɪn/
noun … “measuring how much a split enlightens.”
Information Gain is a metric used in decision tree learning and other machine learning algorithms to quantify the reduction in uncertainty (entropy) about a target variable after observing a feature. It measures how much knowing the value of a specific predictor improves the prediction of the outcome, guiding the selection of the most informative features when constructing decision trees, such as Decision Trees.