One of most excruciating pain points during Data Exploration and Preparation stage of an Analytics project are missing values. How do you deal with missing values - ignore or treat them? The answer would depend on the percentage of those missing values in the dataset, the variables affected by missing values, whether those missing values are a part of dependent or the independent variables, etc. Missing Value treatment becomes important since the data insights or the performance of your predictive model could be impacted if the missing values are not appropriately handled.The 2 tables above give different insights. The inference from the table on the left with the missing data indicates lower count for Android Mobile users and iOS Tablet users and higher Average Transaction Value compared to the inference from the right table with no missing data. The inference from the data with missing values could adversely impact business decisions.

I think we can all agree that this assumption is usually violated in practice (think temporal auto-correlation for instance, as observed when dealing with time series). My question is therefore: where exactly does the i.i.d. I'm asking this because I can think of many models (e.g. Actually the results usually stay the same, it is the inferences that one can draw that change (e.g. My guess is therefore that the i.i.d.

K Means Cluster will be our introduction to Unsupervised Machine Learning. What is Unsupervised Machine Learning exactly? Well, the simplest explanation I can offer is that unlike supervised where our data set contains a result, unsupervised does not. Think of a simple regression where I have the square footage and selling prices (result) of 100 houses. Taking that data, I can easily create a prediction model that will predict the selling price of a house based off of square footage.

This is a short video that contains the criteria that I use while choosing the appropriate clustering algorithm. If you have other criteria that you use, please do let me know by leaving a comment on my blog or by reaching out to me on Twitter @VRaoRao Thanks!

Businesspeople need to demand more from machine learning so they can connect data scientists' work to relevant action. This requires basic machine learning literacy -- what kinds of problems can machine learning solve, and how to talk about those problems with data scientists. Linear regression and feature selection are two such foundational topics. Linear regression is a powerful technique for predicting numbers from other data. Imagine you have an imperative to predict basketball scores from game statistics, and you miraculously know absolutely nothing about basketball. The fact that a hoop is involved is news to you.

Teaching the machines involve a structural process where every stage builds a better version of the machine.

I've seen a dichotomy between "analytics" vs. "data science" (or "statistics) in several teams of web marketing, because people may feel analytics is simple, fast and work well while statistics is hard to learn, complicated and time-consuming. In the latest post, I argued about the dichotomy from a viewpoint of analytic accuracy and pointed out a pitfall of simple and fast analytics. Essentially, any kind of multivariate data may contain third-party effect: but I feel not a few people neglect it. To avoid such a pitfall, multivariate statistics is important.

We consider the problem of community detection or clustering in the labeled Stochastic Block Model (LSBM) with a finite number $K$ of clusters of sizes linearly growing with the global population of items $n$. Every pair of items is labeled independently at random, and label $\ell$ appears with probability $p(i,j,\ell)$ between two items in clusters indexed by $i$ and $j$, respectively. The objective is to reconstruct the clusters from the observation of these random labels. Clustering under the SBM and their extensions has attracted much attention recently. Most existing work aimed at characterizing the set of parameters such that it is possible to infer clusters either positively correlated with the true clusters, or with a vanishing proportion of misclassified items, or exactly matching the true clusters. We find the set of parameters such that there exists a clustering algorithm with at most $s$ misclassified items in average under the general LSBM and for any $s=o(n)$, which solves one open problem raised in \cite{abbe2015community}. We further develop an algorithm, based on simple spectral methods, that achieves this fundamental performance limit within $O(n \mbox{polylog}(n))$ computations and without the a-priori knowledge of the model parameters.

Dimensionality reduction techniques play an essential role in data analytics, signal processing and machine learning. Dimensionality reduction is usually performed in a preprocessing stage that is separate from subsequent data analysis, such as clustering or classification. Finding reduced-dimension representations that are well-suited for the intended task is more appealing. This paper proposes a joint factor analysis and latent clustering framework, which aims at learning cluster-aware low-dimensional representations of matrix and tensor data. The proposed approach leverages matrix and tensor factorization models that produce essentially unique latent representations of the data to unravel latent cluster structure -- which is otherwise obscured because of the freedom to apply an oblique transformation in latent space. At the same time, latent cluster structure is used as prior information to enhance the performance of factorization. Specific contributions include several custom-built problem formulations, corresponding algorithms, and discussion of associated convergence properties. Besides extensive simulations, real-world datasets such as Reuters document data and MNIST image data are also employed to showcase the effectiveness of the proposed approaches.

Asynchronous parallel optimization algorithms for solving large-scale machine learning problems have drawn significant attention from academia to industry recently. This paper proposes a novel algorithm, decoupled asynchronous proximal stochastic gradient descent (DAP-SGD), to minimize an objective function that is the composite of the average of multiple empirical losses and a regularization term. Unlike the traditional asynchronous proximal stochastic gradient descent (TAP-SGD) in which the master carries much of the computation load, the proposed algorithm off-loads the majority of computation tasks from the master to workers, and leaves the master to conduct simple addition operations. This strategy yields an easy-to-parallelize algorithm, whose performance is justified by theoretical convergence analyses. To be specific, DAP-SGD achieves an $O(\log T/T)$ rate when the step-size is diminishing and an ergodic $O(1/\sqrt{T})$ rate when the step-size is constant, where $T$ is the number of total iterations.