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Estimating Lake Water Volume With Regression and Machine Learning Methods


The volume of a lake is a crucial component in understanding environmental and hydrologic processes. The State of Minnesota (USA) has tens of thousands of lakes, but only a small fraction has readily available bathymetric information. In this paper we develop and test methods for predicting water volume in the lake-rich region of Central Minnesota. We used three different published regression models for predicting lake volume using available data. The first model utilized lake surface area as the sole independent variable. The second model utilized lake surface area but also included an additional independent variable, the average change in land surface area in a designated buffer area surrounding a lake. The third model also utilized lake surface area but assumed the land surface to be a self-affine surface, thus allowing the surface area-lake volume relationship to be governed by a scale defined by the Hurst coefficient. These models all utilized bathymetric data available for 816 lakes across the region of study. The models explained over 80% of the variation in lake volumes. The sum difference between the total predicted lake volume and known volumes were <2%. We applied these models to predicting lake volumes using available independent variables for over 40,000 lakes within the study region. The total lake volumes for the methods ranged from 1,180,000- and 1,200,000-hectare meters. We also investigated machine learning models for estimating the individual lake volume...

How to use logistic regression for image classification?


Image Classification is a process of classifying various image categories to their appropriate labels or categories it is associated with. Image classification is mostly employed with Convolutional Neural Networks (CNNs), but this article is an attempt to showcase that even logistic regression has the capability to classify images efficiently with a reduction in computational time and also to waive off the tedious task of building complex models for image classification. Logistic Regression is one of the supervised machine learning algorithms which would be majorly employed for binary class classification problems where according to the occurrence of a particular category of data the outcomes are fixed. Logistic regression operates basically through a sigmoidal function for values ranging between 0 and 1. As mentioned earlier as this article emphasizes using Logistic Regression for Image classification we are using the Hand Sign Digit Classification dataset with two categories of images showing Hand Signs of 0 and 1.

Double Debiased Machine Learning (part 2)


To better understand the source of the bias, in the first part of this post, we have explored the example of a firm that is interested in testing the effectiveness of an ad campaign. The firm has information on its current ad spending and on the level of sales. The problem arises because the firm is uncertain on whether it should condition its analysis on the level of past sales. I import the data generating process dgp_pretest() from src.dgp and some plotting functions and libraries from src.utils. We have data on 1000 different markets, for which we observe current sales, the amount spent in advertisement and past sales.

All About Restaurant Recommender - Analytics Vidhya


This article was published as a part of the Data Science Blogathon. Now imagine the last time when you wished to order some clothes and what you were aware of is that you wish to get a shirt/top with polka dots as they were in fashion but once you are on amazon you struggle in finding the best design as there may be more than millions of polka dot pattern shirt in that case what you prefer is scrolling to recommended part and 40% of times you end up ordering items from recommended tabs. So this problem accounts for major purchases in e-commerce and e-content-based apps, not just these 2 the list goes on and on. This case study is built on datasets provided by It allows customers in Oman to order food from their favorite restaurants and have it delivered to their address.

CS 229 - Machine Learning Tips and Tricks Cheatsheet


In a context of a binary classification, here are the main metrics that are important to track in order to assess the performance of the model. Confusion matrix The confusion matrix is used to have a more complete picture when assessing the performance of a model. ROC The receiver operating curve, also noted ROC, is the plot of TPR versus FPR by varying the threshold. Once the model has been chosen, it is trained on the entire dataset and tested on the unseen test set. Cross-validation Cross-validation, also noted CV, is a method that is used to select a model that does not rely too much on the initial training set.



Now that we have an understanding of Baye's Rule, let's try to use it to analyze linear regression models. Where i is the dimensionality of the data X. Yj is the corresponding output for Xj. If i 3, Yj w1* x1j w2* x2j w3* x3j Where j is ranging from 1 to N where N is the number of data points we have. While the process of Bayesian modelling will be taken up in next part, let us consider the below model as true, for now.

What Happens When You Omit Important Variables From Your Regression Model


We'll study the consequences of failing to include important variables in a linear regression model. Our goal will be to formulate a well-known result in statistical modeling called Omitted Variable Bias and to illustrate the calculation using the sample data set. The following data contains specifications of 205 automobiles taken from the 1985 edition of Ward's Automotive Yearbook. Each row contains a set of 26 specifications about a single vehicle. The above 4-variables version of the data set is available for download from here.

Understanding Conditional Variance and Conditional Covariance


Conditional Variance and Conditional Covariance are concepts that are central to statistical modeling. In this article, we'll learn what they are, and we'll illustrate how to calculate them using a real-world data set. First, a quick refresher on what is variance and covariance. Variance of a random variable measures its variation around its mean. The covariance between two random variables is a measure of how correlated are their variations around their respective means.

Accuracy versus interpretability? With generalized additive models (GAMs), you can have both


In this post, I will provide an overview of generalized additive models (GAMs) and their desirable features. Predictive accuracy has long been an important goal of machine learning. But model interpretability has received more attention in recent years. Stakeholders, such as executives, regulators, and domain experts, often want to understand how and why a model makes its predictions before they trust it enough to use it in practice. However, when you train a machine learning model, you typically face a tradeoff between accuracy and interpretability.