Definition of probability Probability is a word that we use a lot in our everyday life. Winning the lottery and being struck by lightning is very unprobable. Instead, receiving a notification on the phone or encountering traffic on the street…
What is probability theory? Most phenomena in our lives are partially random. Think about weather forecasting, the stock market, and football game outcomes. These are all things we can’t exactly predict, but fortunately, we can spot behaviors through probability theory.…
What is bagging? Bagging is a parallel ensemble learning technique that trains multiple weak models on different datasets and averages their predictions. The bagging algorithm Problem statement We have a dataset and want to build an ensemble model to make…
What is ensemble learning? Ensemble learning is a machine-learning approach where we train numerous simple models and aggregate their predictions. These simple models are called weak learners. What is an ensemble model? As inferred from the definition above, an ensemble…
Suppose we have a graph representing the population of a village as a function of time.Let us take two time instants on the x-axis where the population is equal. Now I ask, when did people live better? There is no…
In this post, I’ll show you how to program a random forest from scratch in Python using ONLY MATH. Why is coding a random forest from scratch useful? When studying a new machine learning model, I always get lost between…
What are missing values in machine learning? Missing values in a dataset indicate the absence of observations. The danger of missing values Why are missing values a problem for our job? Missing values bring mainly 2 problems: Types of missing…
What are categorical features – recap In categorical features, measurements can assimilate a number of limited and fixed values, called “categories“. There are 2 types of categorical features: Why can’t most ML models deal with categorical values? Artificial intelligence models…
What is feature engineering? Feature engineering is selecting, extracting, and transforming features from raw data to create a new dataset useful for building predictive models. This new dataset is compatible with our algorithm and provides the model with more valuable…
Types of classification outputs Positive and negative outputs In a classification problem, there are 2 types of categories, positive and negative. Positive categories are labels with a particular characteristic that we are interested in. All the other categories are negative.…