Introduction Gradient Boosting is an ensemble machine learning model, that - as the name suggests - is based on boosting. An ensemble model based on boosting refers to a model that sequentially builds models, and the new model depends on the previous model. In Gradient Boosting these models are built such that they improve the error of the previous model. These individual models are so-called weak learners, which means they have low predictive skills.

Introduction In this post, we will go through the development of a Gradient Boosting model for a regression problem, considering a simplified example. We calculate the individual steps in detail, which are defined and explained in the separate post Gradient Boost for Regression - Explained. Please refer to this post for a more general and detailed explanation of the algorithm. Data We will use a simplified dataset consisting of only 10 samples, which describes how many meters a person has climbed, depending on their age, whether they like height, and whether they like goats.

Introduction Gradient Boosting, also called Gradient Boosting Machine (GBM) is a type of supervised Machine Learning algorithm that is based on ensemble learning. It consists of a sequential series of models, each one trying to improve the errors of the previous one. It can be used for both regression and classification tasks. In this post, we introduce the algorithm and then explain it in detail for a regression task. We will look at the general formulation of the algorithm and then derive and simplify the individual steps for the most common use case, which uses Decision Trees as underlying models and a variation of the Mean Squared Error (MSE) as loss function.

Introduction AdaBoost is an ensemble model that sequentially builds new models based on the errors of the previous model to improve the predictions. The most common case is to use Decision Trees as base models. Very often the examples explained are for classification tasks. AdaBoost can, however, also be used for regression problems. This is what we will focus on in this post. This article covers the detailed calculations of a simplified example.

Introduction AdaBoost is an example of an ensemble supervised Machine Learning model. It consists of a sequential series of models, each one focussing on the errors of the previous one, trying to improve them. The most common underlying model is the Decision Tree, other models are however possible. In this post, we will introduce the algorithm of AdaBoost and have a look at a simplified example for a classification task using sklearn.

Introduction In Ensemble Learning multiple Machine Learning models are combined into one single prediction to improve the predictive skill. The individual models can be of different types or the same. Ensemble learning is based on “the wisdom of the crowds”, which assumes that the expected value of multiple estimates is more accurate than a single estimate. Ensemble learning can be used for regression or classification tasks. Three main types of Ensemble Learning method are most common.

Introduction A Random Forest is a supervised Machine Learning model, that is built on Decision Trees. To understand how a Random Forest works, you should be familiar with Decision Trees. You can find an introduction in the separate article Decision Trees - Explained. A major disadvantage of Decision Trees is that they tend to overfit and often have difficulties to generalize to new data. Random Forests try to overcome this weakness.

Introduction A Decision Tree is a simple Machine Learning model that can be used for both regression and classification tasks. In the article Decision Trees for Classification - Example a Decision Tree for a classification problem is developed in detail. In this post, we consider a regression problem and build a Decision Tree step by step for a simplified dataset. Additionally, we use sklearn to fit a model to the data and compare the results.

Introduction A Decision Tree is a supervised Machine Learning algorithm that can be used for both regression and classification problems. It is a non-parametric model, which means there is no specific mathematical function underlying to fit the data (in contrast to e.g. Linear Regression or Logistic Regression), but the algorithm only learns from the data itself. Decision Trees learn rules for decision making and used to be drawn manually before Machine Learning came up.

Introduction Feature Selection is the process of determining the most suitable subset of the total number of available features for modeling. It helps to understand which features contribute most to the target data. This is usefull to Improve Model Performance. Redundant and irrelevant features may be misleading for the model. Additionally, if the feature space is too large compared to the sample size. This is called the curse of dimensionality and may reduce the model’s performance.

Introduction In a previous article, we introduced Linear Regression in detail and more generally, showed how to find the best model and discussed its chances and limitations. In this post, we are looking at a concrete example. We are going to calculate the slope and the intercept from a Simple Linear Regression analytically, looking at the example data provided in the next plot. Illustration of a simple linear regression between the body mass and the maximal running speed of an animal.

Introduction Linear Regression is a type of Supervised Machine Learning Algorithm, where a linear relationship between the input feature(s) and the target value is assumed. Linear Regression is a specific type of regression model, where the mapping learned by the model describes a linear function. As in all regression tasks, the target variable is continuous. In a linear regression, the linear relationship between one (Simple Linear Regression) or more (Multiple Linear Regression) independent variable and one dependent variable is modeled.

Regression Problems Regression problems in Machine Learning are a type of supervised learning problem, where a continuous numerical variable is predicted, such as, for example, the age of a person or the price of a product. A special type is the Linear Regression, where a linear relationship between two (Simple Linear Regression or more (Multiple Linear Regression) is analyzed. The example plots in this article will be illustrated with a simple linear regression.