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The subject of classification is one of the important issues in different sciences. Logistic regression is one of the statistical
methods to classify data in which the underlying distribution of the data is assumed to be known. Today, researchers in
addition to statistical methods use other methods such as machine learning in which the distribution of the data does not
need to be known. In this paper, in addition to the logistic regression, some machine learning methods including CART
decision tree, random forest, Bagging and Boosting of supervising learning are introduced. Finally, using four real data
sets, we compare the performance of these algorithms with respect to the accuracy measure.

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Analysis and modeling the‎ ​‎h​igh-dimensional data is one of the most challenging problems faced by the world nowaday‎. ‎Interpretation of such data is not easy and needs to be applied to modern methods‎. The penalized methods are one of the most popular ways to analyze the high-dimensional data‎. ‎Also‎, ‎the regression models and their analysis are affected by the outliers seriously‎. The least trimmed squares method is one of the best robust approaches to solve the corruptive influence of the outliers‎. ‎Semiparametric models‎, ‎which are a combination of both parametric and nonparametric models‎, ‎are very flexible models‎. ‎They are useful when the model contains both parametric and nonparametric parts‎. ‎The main purpose of this paper is to analyze semiparametric models in high-dimensional data with the presence of outliers using the robust sparse Lasso approach‎. ‎Finally‎, ‎the performance of the proposed estimator is examined using a real data analysis about production of vitamin B2‎.

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Today, the diagnosis of diseases using artificial intelligence and machine learning algorithms are of great importance, because by using the data available in the study field of the desired disease, useful information and results can be obtained that reduce the occurrence of many deaths. Among these diseases, we can mention the diagnosis of diabetes, which has spread today due to the growth of urban life and the decrease in people's activity. So, it is very important to know whether a person is suffering from diabetes or not. In this article, the data set related to the information of people who have done the diabetes diagnosis test is used, this information is related to 520 people. People are classified into two groups based on whether their diabetes test result is positive or not, and Bayesian classification methods such as Bayesian Support Vector Machine, Naive Bayes, CNK and CatBoost ensemble classification method have been used to conclude which of these The methods can have a better ability to analyze the data and also to compare these methods use accuracy, precision, F1-score, recall, ROC diagram.

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This study aimed to apply the theory of planned behaviors on entrepreneurial tendencies and the effect of this tendency on the development of information technology among 18-30 years old Iranian youth in the winter of 1401. A part of the sample was based on the age group listed in the characteristics of mobile operators (18-30 years old) in Tehran province who received the questionnaire completely randomly using SMS system and sending the link address, and also another section was a of students aged 18-30 years old of The Islamic Azad University of South Tehran Branch, and the research questionnaire was provided to them. The validity of the questionnaire was confirmed by experts in ICT and its reliability was obtained based on Cronbach's alpha test with an alpha coefficient of at least 0.70 (criterion). The results showed that according to the theory of planned behaviors, entrepreneurial tendency has an effect on information technology development in Iranian youth aged 18-30 years. 

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Customer churn is one of the major economic concerns of many companies, including banks, and banks have focused their attention on customer retention, because the cost of attracting a new customer is much higher than the cost of keeping a customer.
Customer churn prediction and profiling are two major economic concerns for many companies. 
Different learning approaches have been proposed; however, a priori choice of the most suitable model to perform both tasks remains non-trivial as it is highly dependent on the intrinsic characteristics of the churn data. 
Our study compares several machine learning methods with several resampling approaches for data balancing of a public bank data set.
Our evaluations, reported in terms of area under the curve (AUC) and sensitivity, explore the influence of rebalancing strategies and difference machine learning methods. 
This work identifies the most appropriate methods in an attrition context and an effective pipeline based on an ensemble approach and clustering. Our strategy can enlighten marketing or human resources services on the behavioral patterns of customers and their attrition probability.