I just completed a take home assessment as part of the interview process for a company. I was told I didn't pass because my answer lacked proper training and test sets The data set consisted of a mix of categorical and numerical predictors, with the dependent variable being a numerical variable. I then removed all rows with NA values and generated boxplots for each predictor. For one variable, I replaced all of its outliers with the median. For some other variables that indicated percentage values, I did not remove the outliers because they did not seem like obvious outliers (for example, the boxplot showed that values greater than .1 were outliers, but all of those outliers still ranged from 0 to 1 so I didn't think they were typos) I then ran a Lasso linear regression model.

In my previous blog I have explained about linear regression. In today's post I will explain about logistic regression. Consider a scenario where we need to predict a medical condition of a patient (HBP),HAVE HIGH BP or NO HIGH BP, based on some observed symptoms – Age, weight, Issmoking, Systolic value, Diastolic value, RACE, etc.. In this scenario we have to build a model which takes the above mentioned symptoms as input values and HBP as response variable. Note that the response variable (HBP) is a value among a fixed set of classes, HAVE HIGH BP or NO HIGH BP.

Multilevel models are a class of regression models for data that have a hierarchical (or nested) structure. Common examples of such data structures are students nested within schools or classrooms, patients nested within hospitals, or survey respondents nested within countries. Using regression techniques that ignore this hierarchical structure (such as ordinary least squares) can lead to incorrect results because such methods assume that all observations are independent. Perhaps more important, using inappropriate techniques (like pooling or aggregating) prevents researchers from asking substantively interesting questions about how processes work at different levels. This two-day seminar provides an intensive introduction to multilevel models.

Machine learning (ML) models may be deemed confidential due to their sensitive training data, commercial value, or use in security applications. Increasingly often, confidential ML models are being deployed with publicly accessible query interfaces. ML-as-a-service ("predictive analytics") systems are an example: Some allow users to train models on potentially sensitive data and charge others for access on a pay-per-query basis. The tension between model confidentiality and public access motivates our investigation of model extraction attacks. In such attacks, an adversary with black-box access, but no prior knowledge of an ML model's parameters or training data, aims to duplicate the functionality of (i.e., "steal") the model. Unlike in classical learning theory settings, ML-as-a-service offerings may accept partial feature vectors as inputs and include confidence values with predictions. Given these practices, we show simple, efficient attacks that extract target ML models with near-perfect fidelity for popular model classes including logistic regression, neural networks, and decision trees. We demonstrate these attacks against the online services of BigML and Amazon Machine Learning. We further show that the natural countermeasure of omitting confidence values from model outputs still admits potentially harmful model extraction attacks. Our results highlight the need for careful ML model deployment and new model extraction countermeasures.

Machine Learning is rapidly becoming a required and critical component of engineering organizations across the tech industry. From movie recommendation algorithms to self-driving cars, it is clearly an exciting and compelling field. Companies are hiring armies of Machine Learning researchers to solve difficult problems like voice and object recognition. What does this all mean to the average software engineer? In many cases, extremely specialized knowledge is necessary to outperform existing state-of-the-art systems.

This paper proposes a general adaptive procedure for budget-limited predictor design in high dimensions called two-stage Sampling, Prediction and Adaptive Regression via Correlation Screening (SPARCS). SPARCS can be applied to high dimensional prediction problems in experimental science, medicine, finance, and engineering, as illustrated by the following. Suppose one wishes to run a sequence of experiments to learn a sparse multivariate predictor of a dependent variable $Y$ (disease prognosis for instance) based on a $p$ dimensional set of independent variables $\mathbf X=[X_1,\ldots, X_p]^T$ (assayed biomarkers). Assume that the cost of acquiring the full set of variables $\mathbf X$ increases linearly in its dimension. SPARCS breaks the data collection into two stages in order to achieve an optimal tradeoff between sampling cost and predictor performance. In the first stage we collect a few ($n$) expensive samples $\{y_i,\mathbf x_i\}_{i=1}^n$, at the full dimension $p\gg n$ of $\mathbf X$, winnowing the number of variables down to a smaller dimension $l < p$ using a type of cross-correlation or regression coefficient screening. In the second stage we collect a larger number $(t-n)$ of cheaper samples of the $l$ variables that passed the screening of the first stage. At the second stage, a low dimensional predictor is constructed by solving the standard regression problem using all $t$ samples of the selected variables. SPARCS is an adaptive online algorithm that implements false positive control on the selected variables, is well suited to small sample sizes, and is scalable to high dimensions. We establish asymptotic bounds for the Familywise Error Rate (FWER), specify high dimensional convergence rates for support recovery, and establish optimal sample allocation rules to the first and second stages.

Feature selection is a standard approach to understanding and modeling high-dimensional classification data, but the corresponding statistical methods hinge on tuning parameters that are difficult to calibrate. In particular, existing calibration schemes in the logistic regression framework lack any finite sample guarantees. In this paper, we introduce a novel calibration scheme for penalized logistic regression. It is based on simple tests along the tuning parameter path and satisfies optimal finite sample bounds. It is also amenable to easy and efficient implementations, and it rivals or outmatches existing methods in simulations and real data applications.

The question of which model type to apply to a Machine Learning task can be a daunting one given the immense number of algorithms available in the literature. It can be difficult to compare the relative merits of two methods, as one can outperform the other in a certain class of problems while consistently coming in behind for another class. In this post, the last one of our series of posts about Logistic Regression, we'll explore the differences between Decision Trees and Logistic Regression for classification problems, and try to highlight scenarios where one might be recommended over the other. Logistic Regression and trees differ in the way that they generate decision boundaries i.e. the lines that are drawn to separate different classes. To illustrate this difference, let's look at the results of the two model types on the following 2-class problem: Decision Trees bisect the space into smaller and smaller regions, whereas Logistic Regression fits a single line to divide the space exactly into two.

Python is one of the fastest-growing platforms for applied machine learning. In this mini-course, you will discover how you can get started, build accurate models and confidently complete predictive modeling machine learning projects using Python in 14 days. This is a big and important post. You might want to bookmark it. Python Machine Learning Mini-Course Photo by Dave Young, some rights reserved.

As the release of Spark 2.0 finally came, the machine learning library of Spark has been changed from the mllib to ml. One of the biggest change in the new ml library is the introduction of so-called machine learning pipeline. It provides a high level abstraction of the machine learning flow and greatly simplified the creation of machine learning process. In this tutorial, we will walk through the steps on how to create a machine learning pipeline and also explain what is under the hood in the pipeline. In this tutorial, we will demonstrate the process to create a pipeline in Spark to predict airline flight delay.