Logistic Regression is a Supervised Machine Learning Algorithm utilized for classification. Examples for classification include: Email spam or ham, will buy or not buy a product, disease predictions such as cancerous or noncancerous cells. Logistic regression is a Probability problem. Meaning that the outcome of the algorithm is between 0 and 1. It maintains a threshold value to classify the data points(samples).

Despite their high predictive performance, random forest and gradient boosting are often considered as black boxes or uninterpretable models which has raised concerns from practitioners and regulators. As an alternative, we propose in this paper to use partial linear models that are inherently interpretable. Specifically, this article introduces GAM-lasso (GAMLA) and GAM-autometrics (GAMA), denoted as GAM(L)A in short. GAM(L)A combines parametric and non-parametric functions to accurately capture linearities and non-linearities prevailing between dependent and explanatory variables, and a variable selection procedure to control for overfitting issues. Estimation relies on a two-step procedure building upon the double residual method. We illustrate the predictive performance and interpretability of GAM(L)A on a regression and a classification problem. The results show that GAM(L)A outperforms parametric models augmented by quadratic, cubic and interaction effects. Moreover, the results also suggest that the performance of GAM(L)A is not significantly different from that of random forest and gradient boosting.

In this paper, we analyze the generalization performance of the Iterative Hard Thresholding (IHT) algorithm widely used for sparse recovery problems. The parameter estimation and sparsity recovery consistency of IHT has long been known in compressed sensing. From the perspective of statistical learning, another fundamental question is how well the IHT estimation would predict on unseen data. This paper makes progress towards answering this open question by introducing a novel sparse generalization theory for IHT under the notion of algorithmic stability. Our theory reveals that: 1) under natural conditions on the empirical risk function over $n$ samples of dimension $p$, IHT with sparsity level $k$ enjoys an $\mathcal{\tilde O}(n^{-1/2}\sqrt{k\log(n)\log(p)})$ rate of convergence in sparse excess risk; 2) a tighter $\mathcal{\tilde O}(n^{-1/2}\sqrt{\log(n)})$ bound can be established by imposing an additional iteration stability condition on a hypothetical IHT procedure invoked to the population risk; and 3) a fast rate of order $\mathcal{\tilde O}\left(n^{-1}k(\log^3(n)+\log(p))\right)$ can be derived for strongly convex risk function under proper strong-signal conditions. The results have been substantialized to sparse linear regression and sparse logistic regression models to demonstrate the applicability of our theory. Preliminary numerical evidence is provided to confirm our theoretical predictions.

In this article, I will try to interpret the Linear Regression, Lasso, and Decision Tree models which are inherently interpretable. I will analyze global interpretability -- which analyzes the most important feature for prediction in general and local interpretability -- which explains individual prediction results. Machine learning models are used in applications such as fraud and risk detection in bank transactions, voice assistants, recommendation systems, chatbots, self-driving cars, social network analysis, etc. However, sometimes it is difficult to interpret them because the algorithm represents a black box(e.g. So we need additional techniques to analyze black box decisions.

In this blog post, I would like to continue my series on "building from scratch." I will discuss a linear classifier called Logistic Regression. After the discussion of the theoretical concepts we will dive into the code. So, without a further adieu let's start the discussion with the basics of a classifier. A classifier is an estimator that assigns a class label to the input data point.

`Double descent' delineates the generalization behaviour of models depending on the regime they belong to: under- or over-parameterized. The current theoretical understanding behind the occurrence of this phenomenon is primarily based on linear and kernel regression models -- with informal parallels to neural networks via the Neural Tangent Kernel. Therefore such analyses do not adequately capture the mechanisms behind double descent in finite-width neural networks, as well as, disregard crucial components -- such as the choice of the loss function. We address these shortcomings by leveraging influence functions in order to derive suitable expressions of the population loss and its lower bound, while imposing minimal assumptions on the form of the parametric model. Our derived bounds bear an intimate connection with the spectrum of the Hessian at the optimum, and importantly, exhibit a double descent behaviour at the interpolation threshold. Building on our analysis, we further investigate how the loss function affects double descent -- and thus uncover interesting properties of neural networks and their Hessian spectra near the interpolation threshold.

The analysis of high-dimensional probability distributio ns is a central challenge in modern Statistics and Machine Learning. This i s particularly true in the context of Bayesian Statistics, where scientists carry out inferen ce based on the posterior distribution. In modern applications, the posterior distribution is typi cally high-dimensional, and analytically intractable. V ariational Inference (VI) has emerge d as an attractive option to approximate these intractable distributions, facilitating fast, parallel computations in state-of-the-art applications [ 32, 10 ]. In this approach, the distribution of interest is approxi mated (in KL divergence) by distributions from a pre-specified, more tract able collection. The simplest version of VI is the Naive Mean-field approximation (NMF), where the distribution of interest is approximated by a product distribution.

Predicting the stock market is one of the most important applications of Machine Learning in finance. In this article, I will take you through a simple Data Science project on Stock Price Prediction using Machine Learning Python. At the end of this article, you will learn how to predict stock prices by using the Linear Regression model by implementing the Python programming language. Predicting the stock market has been the bane and goal of investors since its inception. Every day billions of dollars are traded on the stock exchange, and behind every dollar is an investor hoping to make a profit in one way or another.

The Machine Learning Crash Course with TensorFlow APIs is a self-study guide for aspiring machine learning practitioners. Learn how to build Machine Learning projects in this TensorFlow Course created by The Click Reader. In this course, you will be learning about Scalar as well as Tensors and how to create them using TensorFlow. You will also be learning how to perform various kinds of Tensor operations for manipulating and changing tensor values. You will be learning how to create a Linear Regression model from scratch using TensorFlow.

Added: Object-Oriented Programming (OOP) for complete Beginners: with real-world examples and in a way that everyone understands OOP! This is the first-ever comprehensive Python Course for Business and Finance Professionals. You will learn and master Python from Zero and the full Python Data Science Stack with real Examples and Projects taken from the Business and Finance world. You will understand and master all required theoretical concepts behind the projects and the code from scratch. Important: the quality Benchmark for the theory part is the CFA (Chartered Financial Analyst) Curriculum.