Hello, I am working on a highly imbalanced dataset (negative examples over 20K and positive examples about 100). I am trying to build a logistic regression model. My current approach includes undersampling of negative examples. However with this approach there are a couple of problems: 1) Several LR models are possible with different samples. How to generalize these models and interpret the output?

Regression is a time-tested manner for approximating relationships among a given collection of data, and the recipient of unhelpful naming via unfortunate circumstances. Linear regression is a simple algebraic tool which attempts to find the "best" (generally straight) line fitting 2 or more attributes, with one attribute (simple linear regression), or a combination of several (multiple linear regression), being used to predict another, the class attribute. A set of training instances is used to compute the linear model, with one attribute, or a set of attributes, being plotted against another. The model then attempts to identify where new instances would lie on the regression line, given a particular class attribute. It is often confusing for people without a sufficient math background to understand how matrix multiplication fits into linear regression.

Founded in 2010, Kaggle is a Data Science platform where users can share, collaborate, and compete. One key feature of Kaggle is "Competitions", which offers users the ability to practice on real world data and to test their skills with, and against, an international community. This guide will teach you how to approach and enter a Kaggle competition, including exploring the data, creating and engineering features, building models, and submitting predictions. We'll follow these steps to a successful Kaggle Competition submission: We need to acquire the data for the competition. The descriptions of the features and some other helpful information are contained in a file with an obvious name, data_description.txt. Download the data and save it into a folder where you'll keep everything you need for the competition. We will first look at the train.csv After we've trained a model, we'll make predictions using the test.csv

In the recent years, reciprocal link prediction has received some attention from the data mining and social network analysis researchers, who solved this problem as a binary classification task. However, it is also important to predict the interval time for the creation of reciprocal link. This is a challenging problem for two reasons: First, the lack of effective features, because well-known link prediction features are designed for undirected networks and for the binary classification task, hence they do not work well for the interval time prediction; Second, the presence of censored data instances makes the traditional supervised regression methods unsuitable for solving this problem. In this paper, we propose a solution for the reciprocal link interval time prediction task. We map this problem into survival analysis framework and show through extensive experiments on real-world datasets that, survival analysis methods perform better than traditional regression, neural network based model and support vector regression (SVR).

In this blog post, I'll help you get started using Apache Spark's spark.ml Classification is a family of supervised machine learning algorithms that identify which category an item belongs to (for example, whether a cancer tissue observation is malignant or not), based on labeled examples of known items (for example, observations known to be malignant or not). Classification takes a set of data with known labels and pre-determined features and learns how to label new records based on that information. Features are the "if questions" that you ask. The label is the answer to those questions.

Supervised machine learning builds a model that makes predictions based on evidence in the presence of uncertainty. A supervised learning algorithm takes a known set of input data and known responses to the data (output) and trains a model to generate reasonable predictions for the response to new data. Use supervised learning if you have known data for the output you are trying to predict. Supervised learning uses classification and regression techniques to develop predictive models. Classification techniques predict discrete responses--for example, whether an email is genuine or spam, or whether a tumor is cancerous or benign.

We propose a penalized likelihood method that simultaneously fits the multinomial logistic regression model and combines subsets of the response categories. The penalty is non differentiable when pairs of columns in the optimization variable are equal. This encourages pairwise equality of these columns in the estimator, which corresponds to response category combination. We use an alternating direction method of multipliers algorithm to compute the estimator and we discuss the algorithm's convergence. Prediction and model selection are also addressed.

The article studies the advantage of Support Vector Regression (SVR) over Simple Linear Regression (SLR) models. SVR uses the same basic idea as Support Vector Machine (SVM), a classification algorithm, but applies it to predict real values rather than a class. SVR acknowledges the presence of non-linearity in the data and provides a proficient prediction model. Along with the thorough understanding of SVR, we also provide the reader with hands on experience of preparing the model on R. We perform SLR and SVR on the same dataset and make a comparison. The article is organized as follows; Section 1 provides a quick review of SLR and its implementation on R. Section 2 discusses the theoretical aspects of SVR and the steps to fit SVR on R. It also covers the basics of tuning SVR model.

A key limitation of sampling algorithms for approximate inference is that it is difficult to quantify their approximation error. Widely used sampling schemes, such as sequential importance sampling with resampling and Metropolis-Hastings, produce output samples drawn from a distribution that may be far from the target posterior distribution. This paper shows how to upper-bound the symmetric KL divergence between the output distribution of a broad class of sequential Monte Carlo (SMC) samplers and their target posterior distributions, subject to assumptions about the accuracy of a separate gold-standard sampler. The proposed method applies to samplers that combine multiple particles, multinomial resampling, and rejuvenation kernels. The experiments show the technique being used to estimate bounds on the divergence of SMC samplers for posterior inference in a Bayesian linear regression model and a Dirichlet process mixture model.

It's been a long time since I did a coding demonstrations so I thought I'd put one up to provide you a logistic regression example in Python! Admittedly, this is a cliff notes version, but I hope you'll get enough from what I have put up here to at least feel comfortable with the mechanics of doing logistic regression in Python (more specifically; using scikit-learn, pandas, etc…). This logistic regression example in Python will be to predict passenger survival using the titanic dataset from Kaggle. Before launching into the code though, let me give you a tiny bit of theory behind logistic regression. The logistic regression formula is derived from the standard linear equation for a straight line.