Code2vec: Learning Distributed Representations of Code

Survey on establishing the optimal number of factors in exploratory factor analysis applied to data mining

In many types of researches and studies including those performed by the sciences of agriculture and plant sciences, large quantities of data are frequently obtained that must be analyzed using different data mining techniques. Sometimes data mining involves the application of different methods of statistical data analysis. Exploratory Factor Analysis (EFA) is frequently used as a technique for data reduction and structure detection in data mining. In our survey, we study the EFA applied to data mining, focusing on the problem of establishing of the optimal number of factors to be retained. The number of factors to retain is the most important decision to take after the factor extraction in EFA. Many researchers discussed the criteria for choosing the optimal number of factors. Mistakes in factor extraction may consist in extracting too few or too many factors. An inappropriate number of factors may lead to erroneous conclusions. A comprehensive review of the state-of-the-art related to this subject was made. The main focus was on the most frequently applied factor selection methods, namely Kaiser Criterion, Cattell’s Scree test, and Monte Carlo Parallel Analysis. We have highligthed the importance of the analysis in some research, based on the research specificity, of the total cumulative variance explained by the selected optimal number of extracted factors. It is necessary that the extracted factors explain at least a minimum threshold of cumulative variance. ExtrOptFact algorithm presents the steps that must be performed in EFA for the selection of the optimal number of factors. For validation purposes, a case study was presented, performed on data obtained in an experimental study that we made on Brassica napus plant. Applying the ExtrOptFact algorithm for Principal Component Analysis can be decided on the selection of three components that were called Qualitative, Generative, and Vegetative, which explained 92% of the total cumulative variance.

Naive Bayes Classification using Scikit-learn

Suppose you are a product manager, you want to classify customer reviews in positive and negative classes. Or As a loan manager, you want to identify which loan applicants are safe or risky? As a healthcare analyst, you want to predict which patients can suffer from diabetes disease. All the examples have the same kind of problem to classify reviews, loan applicants, and patients. Naive Bayes is the most straightforward and fast classification algorithm, which is suitable for a large chunk of data. Naive Bayes classifier successfully used in various application such as spam filtering, text classification, sentiment analysis, and recommender systems. It uses Bayes theorem of probability for prediction of unknown class.

Automated Dashboard with various correlation visualizations in R

In this article, you learn how to make Automated Dashboard with various correlation visualizations in R. First you need to install the rmarkdown rmarkdown package into your R library. Assuming that you installed the rmarkdown rmarkdown , next you create a new rmarkdown rmarkdown script in R.

Writing better code with pytorch and einops

Below are some fragments of code taken from official tutorials and popular repositories (fragments taken for educational purposes, sometimes shortened). For each fragment an enhanced version proposed with comments. In most examples, einops was used to make things less complicated. But you’ll also find some common recommendations and practices to improve the code.