Clustering with Machine Learning — A Comprehensive Guide
The term clustering (in machine learning) refers to the grouping of data: The eponymous clusters. In contrast to data classification, these are not determined by certain common features but result from the spatial similarity of the observed objects (data points/observations). Similarity refers to…
Machine Learning Clustering in Python
This article, together with the code, has also been published in a Jupyter notebook. In this article, we show different methods for clustering in Python. Clustering is the combination of different objects in groups of similar objects. For example, the segmentation of different groups of buyers in retail. Clustering compares the individual properties of an […]
Machine Learning Performance Indicators
In this article, we present a set of metrics that can be used to compare and evaluate different methods and trained models for classification problems. In our article, Machine Learning Classification in Python – Part 1: Data Profiling and Preprocessing, we introduce different classification methods whose performance can be compared with the metrics described here. The […]
Machine Learning Classification in Python – Part 2: Model Implementation and Performance Determination
This is the second part of our series Automated Classification in Python where we use several methods to classify the UCI Machine Learning record “Adult”. In this article, after generating data from Part 1, we will discuss in more detail how we implement the different models and then compare their performance. Classification We use different scikit-learn models for the classification. The […]
Machine Learning Classification in Python – Part 1: Data Profiling and Preprocessing
This is the first part of the series, Automated Classification in Python, in which we demonstrate how to classify a given data set using machine learning classification techniques. In the following article, we show the analysis and processing of the freely available “Adult” data set for classification. We have also published our script together with the […]
Model Validation and Overfitting
The model validation procedure describes the method of checking the performance of a statistical or data-analytical model. A common method for validating neural networks is k-fold cross-validation. In doing so one divides the training data set into k subsets. One of the subsets represents the test set. The remaining subsets then serve as the training […]
Clustering with Machine Learning
The clustering problem is the grouping of objects or data into clusters. In contrast to classification, they are not predetermined but result from the similarity of the different objects considered. Similarity means the spatial distance of the objects, represented as vectors. There are different methods to determine this distance, e.g. the Euclidean or Minkowski metrics. […]