Automated Machine Learning (AutoML) is a service available within the Azure Machine Learning (AML) service.
As the name suggests, the main goal of AutoML is that of automating the development and scoring of different types of ML models to eventually pick the one which is the most performing. Basically, AutoML…
Invented in 1958 at the Cornell Aeronautical Laboratory by Frank Rosenblatt, the Perceptron is a binary classification algorithm that falls within the cluster of Neural Networks algorithms.
More specifically, a Perceptron is a single-layer, feedforward Neural Network whose capability is limited to binary and linear classification tasks. …
In Part 1 of this series, we saw one important splitting criterion for Decision Tree algorithms, that is Information Gain.
In this Part 2 of this series, I’m going to dwell on another splitting criterion, always based on the concept of node heterogeneity, which is the Gini Index. …
Decision Trees are popular Machine Learning algorithms used for both regression and classification tasks. Their popularity mainly arises from their interpretability and representability, as they mimic the way the human brain takes decisions.
The mechanism behind decision trees is that of a recursive classification procedure as a function of explanatory…
Box Plots are very useful graphs used in descriptive statistics. Box plots visually show many features of numerical data through displaying their statistics, like means, averages, and so forth.
Visually speaking, a Box Plot looks like the following:
Announced in September 2020 at Microsoft Ignite, Azure Defender has been presented as the evolution of Azure Security Center (ASC). …
Artificial Intelligence and its applications have opened innovative paths in societies and organizations. Today we can simultaneously get a transcript, in any language, of someone’s speech, identify individuals’ identities via smart cameras, and so on.
Yet it all comes with a price: time and resources. Indeed, training algorithms behind any…
In the previous articles of this series, we have been introducing some techniques to deal with the imbalance in data in binary classification tasks. Part 1 examined some resampling techniques; Part 2 focused on how to modify the algorithm by changing the threshold value.
In this Part 3, we are…
In Part 1 of this series of articles, I’ve been introducing the curse of class imbalance in binary classification tasks and some remedies to address it. More specifically, I’ve been focusing on how to intervene directly to the dataset with different sampling techniques in order to make it more balanced.
…
Whenever we initialize a task for a Machine Learning model, the very first thing to do is analyzing and reasoning on the data we are provided with and will be using for training/testing purposes. …
