You have been observing that since the past few years, happy employees are the key profit generators of your company and in all these years, you noted down the happiness index of all your employees and their productivity. Now you have tons of this employees' data just lying around in excel files and you just recently heard "Data is the new oil. The companies that will win are using math." You are wondering if you could also win by somehow mathifying this data that could predict the productivity of your new employees based on their happiness index. So that it would become easier for you to identify your least productive employees (and then supposedly fire them-just supposedly).

We live in a start of revolutionized era due to development of data analytics, large computing power, and cloud computing. Machine learning will definitely have a huge role there and the brains behind Machine Learning is based on algorithms. This article covers 10 most popular Machine Learning Algorithms which uses currently. These algorithms can be categorized into 3 main categories. Following algorithms are going to be covered in this article.

So I just have to buy a Tableau license and I'm now a data scientist? Okay, let's just take that sales pitch with a grain of salt. I may be clueless, but I know there is more to data science than making pretty visualizations. I can do that in Excel. You got to admit it is slick marketing though. Charting data is the fun stage, and they leave out the painful and time-consuming parts of working with data: cleaning, wrangling, transforming, and loading it. God help you if you need your own custom domain logic when using closed tools. Yes, and that is why I suspect there is value in learning to code. Maybe you can learn Alteryx.

So I just have to buy a Tableau license and I'm now a data scientist? Okay, let's just take that sales pitch with a grain of salt. I may be clueless, but I know there is more to data science than making pretty visualizations. I can do that in Excel. You got to admit it is slick marketing though. Charting data is the fun stage, and they leave out the painful and time-consuming parts of working with data: cleaning, wrangling, transforming, and loading it. God help you if you need your own custom domain logic when using closed tools. Yes, and that is why I suspect there is value in learning to code. Maybe you can learn Alteryx.

Abstract--The Interaction-Transformation (IT) is a new representation for Symbolic Regression that restricts the search space into simpler, but expressive, function forms. This representation has the advantage of creating a smoother search space unlike the space generated by Expression Trees, the common representation used in Genetic Programming. This paper introduces an Evolutionary Algorithmcapable of evolving a population of IT expressions supported only by the mutation operator. The results show that this representation is capable of finding better approximations to real-world data sets when compared to traditional approaches and a state-of-the-art Genetic Programming algorithm. I. INTRODUCTION Regression analysis has the objective of describing the relationship between measurable variables [1]. This analysis can be used to make predictions of not yet observed samples, to study a system's behavior or to calculate the statistical properties of such system. F. O. de Franca is with Federal University of ABC, Center for Mathematics, Computationand Cognition, Heuristics, Analysis and Learning Laboratory, São Paulo, Brazil, email: folivetti@ufabc.edu.br,

Linear regression is perhaps one of the most well-known and well-understood algorithms in statistics and machine learning. Predictive modeling is primarily concerned with minimizing the error of a model or making the most accurate predictions possible, at the expense of explainability. We will borrow, reuse and steal algorithms from many different fields, including statistics and use them towards these ends. The representation of linear regression is an equation that describes a line that best fits the relationship between the input variables (x) and the output variables (y), by finding specific weightings for the input variables called coefficients (B). We will predict y given the input x and the goal of the linear regression learning algorithm is to find the values for the coefficients B0 and B1.

In vanilla linear regression, one (implicitly) assumes that each sample is a linear measurement of a single unknown vector, which needs to be recovered from these measurements. Statistically, it is typically studied in the setting where the samples come from such a ground truth unknown vector, and we are interested in the (computational/statistical complexity of) recovery of this ground truth vector. Mixed linear regression (MLR for brevity) is the problem where there are multiple unknown vectors, and each sample can come from any one of them (and we do not know which one, a-priori). Our objective is again to recover all (or some, or one) of them from the samples. In this paper we consider MLR with the additional presence of corruptions - i.e. adversarial additive errors in the responses - for some unknown subset of the samples. There is now a healthy and quickly growing body of work on algorithms, and corresponding theoretical guarantees, for MLR with and without additive noise and corruptions; we review these in detail in the related work section. In our paper we start from a classical (but hard to compute) approach from robust statistics: least trimmed squares [Rou84]. This advocates fitting a model so as to minimize the loss on only a fraction τ of the samples, instead of all of them - but crucially, the subset S of samples chosen and the model to fit them are to be estimated jointly.

The increasing adoption of machine learning tools has led to calls for accountability via model interpretability. But what does it mean for a machine learning model to be interpretable by humans, and how can this be assessed? We focus on two definitions of interpretability that have been introduced in the machine learning literature: simulatability (a user's ability to run a model on a given input) and "what if" local explainability (a user's ability to correctly indicate the outcome to a model under local changes to the input). Through a user study with 1000 participants, we test whether humans perform well on tasks that mimic the definitions of simulatability and "what if" local explainability on models that are typically considered locally interpretable. We find evidence consistent with the common intuition that decision trees and logistic regression models are interpretable and are more interpretable than neural networks. We propose a metric - the runtime operation count on the simulatability task - to indicate the relative interpretability of models and show that as the number of operations increases the users' accuracy on the local interpretability tasks decreases.

In many applications, we want to predict and estimate the effect of a covariate on survival timeof interests. Examples include treatment, surgical procedure, or immunization on survival time of patients, who for example, could be individuals who have metastatic breast cancer, military casualties suffering from various injuries, or survival time of infectious diseases.Classically, most datasets have been too small to meaningfully examine the heterogeneity of the data beyond dividing them into a few subpopulations. In the past few years, however, there has been an explosion of experimental settings where it is potentially feasible to explore heterogeneity to its full extent. An impediment to exploring heterogeneous effects is the fear that scientists with two opposite agendas could hypothetically string together two opposite but coherent results by searching through many different possible models and then reporting only the very extreme ones - highlighting solely spurious results (Olken, 2015). Thus, protocols for clinical trials must specify in advance the pre-analysis plans and then learn from the data.

We derive explicit Mixed Integer Optimization (MIO) constraints, as opposed to iteratively imposing them in a cutting plane framework, that impose significance and avoid multicollinearity for building linear regression models. In this way we extend and improve the research program initiated in Bertsimas and King (2016) that imposes sparsity, robustness, pairwise collinearity and group sparsity explicitly and significance and avoiding multicollinearity iteratively. We present a variety of computational results on real and synthetic datasets that suggest that the proposed MIO has a significant computational edge compared to Bertsimas and King (2016) in accuracy, false detection rate and computational time in accounting for significance and multicollinearity as well as providing a holistic framework to produce regression models with desirable properties a priori.