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Machine Learning Algorithms Cheat Sheet

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Machine learning is a subfield of artificial intelligence (AI) and computer science that focuses on using data and algorithms to mimic the way people learn, progressively improving its accuracy. This way, Machine Learning is one of the most interesting methods in Computer Science these days, and it's being applied behind the scenes in products and services we consume in everyday life. In case you want to know what Machine Learning algorithms are used in different applications, or if you are a developer and you're looking for a method to use for a problem you are trying to solve, keep reading below and use these steps as a guide. Machine Learning can be divided into three different types of learning: Unsupervised Learning, Supervised Learning, and Semi-supervised Learning. Unsupervised learning uses information data that is not labeled, that way the machine should work with no guidance according to patterns, similarities, and differences. On the other hand, supervised learning has a presence of a "teacher", who is in charge of training the machine by labeling the data to work with. Next, the machine receives some examples that allow it to produce a correct outcome.


Which models are interpretable?

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Data Scientists have the role to extract information from raw data. They aren't engineers, nor they are software developers. They dig inside data and extract the gold from the mine. Knowing what a model does and how it works is part of this job. Black-boxes models, although sometimes work better than other models, aren't a good idea if we need to learn something from our data.


XGBoost Alternative Base Learners

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XGBoost, short for "Extreme Gradient Boosting," is one of the strongest machine learning algorithms for handling tabular data, a well-deserved reputation due to its success in winning numerous Kaggle competitions. XGBoost is an ensemble machine learning algorithm that usually consists of Decision Trees. The Decision Trees that make up XGBoost are individually referred to as gbtree, short for "gradient boosted tree." The first Decision Tree in the XGBoost ensemble is the base learner whose mistakes all subsequent trees learn from. Although Decision Trees are generally preferred as base learners due to their excellent ensemble scores, in some cases, alternative base learners may outperform them.


GitHub - ml-jku/hopular: Hopular: Modern Hopfield Networks for Tabular Data

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While Deep Learning excels in structured data as encountered in vision and natural language processing, it failed to meet its expectations on tabular data. For tabular data, Support Vector Machines (SVMs), Random Forests, and Gradient Boosting are the best performing techniques with Gradient Boosting in the lead. Recently, we saw a surge of Deep Learning methods that were tailored to tabular data but still underperformed compared to Gradient Boosting on small-sized datasets. We suggest "Hopular", a novel Deep Learning architecture for medium- and small-sized datasets, where each layer is equipped with continuous modern Hopfield networks. The modern Hopfield networks use stored data to identify feature-feature, feature-target, and sample-sample dependencies.


How Random Forests & Decision Trees Decide: Simply Explained With An Example In Python

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Let's assume that we have a labeled dataset with 10 samples in total. What the Decision Trees do is simple: they find ways to split the data in a way such as that separate as much as possible the samples of the classes (increasing the class separability). In the above example, the perfect split would be a split at x 0.9 as this would lead to 5 red points being at the left side and the 5 blue at the right side (perfect class separability). Each time we split the space/data like that, we actually build a decision tree with a specific rule. Here we initially have the root node containing all the data and then, we split the data at x 0.9 leading to two branches leading to two leaf nodes.


A Complete Guide to Decision Trees

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The Decision Tree is a machine learning algorithm that takes its name from its tree-like structure and is used to represent multiple decision stages and the possible response paths. The decision tree provides good results for classification tasks or regression analyses. With the help of the tree structure, an attempt is made not only to visualize the various decision levels but also to put them in a certain order. For individual data points, predictions can be made, for example, a classification by arriving at the target value along with the observations in the branches. The decision trees are used for classifications or regressions depending on the target variable.


Forecasting Recessions With Scikit-Learn

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It is no secret that everybody wants to predict recessions. Many economists and finance firms have attempted this with limited success, but by and large there are several well known leading indicators for recessions in the US economy. However, when presented to the general public these indicators are typically taken alone, and are not framed in a way that can give probability statements associated with an upcoming recession. In this project, I have taken several of those economic indicators and built a classification model to generate probabilistic statements. Here, the actual classification ('recession' or'no recession') is not as important as the probability of a recession, since this probability will be used to determine a basic portfolio scheme which I will describe later on.


A new approach for determining SARS-CoV-2 epitopes using machine learning-based in silico methods

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A new approach (SMOTE-RF-SVM) is proposed to identify SARS-CoV-2 epitopes that can be used in vaccine design. Epitope candidates that can be used in vaccine design were determined using machine learning-based in silico and bioinformatics tools. In the unbalanced dataset, generating artificial data with the SMOTE technique increased the model performance. Nonallergic, high antigen (antigen score ≥1.0) and nontoxic 11 possible epitopes candidates were proposed. The search space for vaccine studies was narrowed by SMOTE-RF-SVM.


Pruned Random Forests for Effective and Efficient Financial Data Analytics

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It is evident that Machine Learning (ML) has touched all walks of our lives! From checking the weather forecast to applying for a loan or a credit card, ML is used in almost every aspect of our daily life. In this chapter, ML is explored in terms of algorithms and applications. Special consideration is given to ML applications in the financial data analytics domain including stock market analysis, fraud detection in financial transactions, credit risk analysis, loan defaulting rate analysis, and profit–loss analysis. The chapter establishes the significance of Random Forests as an effective machine learning method for a wide variety of financial applications.


Introduction to Random Forest Algorithm

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Random Forest is a supervised machine learning algorithm that is composed of individual decision trees. This type of model is called an ensemble model because an "ensemble" of independent models is used to compute a result. The basis for the Random Forest is formed by many individual decision trees, the so-called Decision Trees. A tree consists of different decision levels and branches, which are used to classify data. The Decision Tree algorithm tries to divide the training data into different classes so that the objects within a class are as similar as possible and the objects of different classes are as different as possible. This tree helps to decide whether to do sports outside or not, depending on the weather variables "weather", "humidity" and "wind force".