With a ROC curve, you're trying to find a good model that optimizes the trade off between the False Positive Rate (FPR) and True Positive Rate (TPR). What counts here is how much area is under the curve (Area under the Curve AuC). The ideal curve in the left image fills in 100%, which means that you're going to be able to distinguish between negative results and positive results 100% of the time (which is almost impossible in real life). The further you go to the right, the worse the detection. The ROC curve to the far right does a worse job than chance, mixing up the negatives and positives (which means you likely have an error in your setup).
Handwritten Digit Recognition is an interesting machine learning problem in which we have to identify the handwritten digits through various classification algorithms. There are a number of ways and algorithms to recognize handwritten digits, including Deep Learning/CNN, SVM, Gaussian Naive Bayes, KNN, Decision Trees, Random Forests, etc. In this article, we will deploy a variety of machine learning algorithms from the Sklearn's library on our dataset to classify the digits into their categories. The dataset contains a total of 1797 sample points. The DESCR provides a description of the dataset.
This article was published as a part of the Data Science Blogathon. Many times we have come across this statement – Lasso regression causes sparsity while Ridge regression doesn't! But I'm pretty sure that most of us might not have understood how exactly this works. Let's try to understand this using calculus. First, let's understand what sparsity is.
Many real-world problems involve datasets where only some of the data is labeled and the rest is unlabeled. In this post, we discuss our implementation of semi-supervised learning for predicting the synthesizability of theoretical materials. When we think about the materials that will enable next-generation technologies, it's probably not the case that there is one ultimate material waiting to be found that will solve all our problems. The problems we need to solve (producing and storing clean energy, mitigating climate change, desalinating water, etc.) are complex and varied. Even zooming in to the next-generation of electronics, computers, and nanotechnology, there probably isn't a single perfect material to exploit in the same way that silicon has been used in all our familiar devices.
It is now a well-established fact that data science jobs are on an exponential rise. With companies trying to analyze data to gain valuable insights, understand trends and more, data science roles, like data scientists, data engineers, data analysts, analytics specialists, consultants, insights analysts, and more are in high demand than ever. No wonder that Harvard Business Review has named it as the sexiest job of the 21st Century in October 2012. However, preparing for a data science job position can be intimidating. While it is often suggested that the key to crack such an interview is having technical preparation about technology and possessing technological aptitude.
So, what is cross validation? Recalling my post about model selection, where we saw that it may be necessary to split data into three different portions, one for training, one for validation (to choose among models) and eventually measure the true accuracy through the last data portion. This procedure is one viable way to choose the best among several models. Cross validation (CV) is not too different from this idea, but deals with the model training/validation in quite a smart way. For CV we use a larger combined training and validation data set, followed by a testing dataset.
Machine Learning interview questions is the essential part of Data Science interview and your path to becoming a Data Scientist. I've divided this guide to machine learning interview questions and answers into the categories so that you can more easily get to the information you need when it comes to machine learning questions. Supervised learning requires training using labelled data. For example, in order to do classification, which is a supervised learning task, you'll first need to label the data you'll use to train the model to classify data into your labelled groups. Unsupervised learning, in divergence, does not require labeling data explicitly.
Logistic Regression is the most widely used classification algorithm in machine learning. It is used in many real-world scenarios like spam detected, cancer detection, IRIS dataset, etc. Mostly it is used in binary classification problems. But it can also be used in multiclass classification. Logistic Regression predicts the probability that the given data point belongs to a certain class or not. In this article, I will be using the famous heart disease dataset from Kaggle.
In order to convey the results of an analysis to the management, a'cumulative response curve' is used, which is more intuitive than the ROC curve. A ROC curve is very difficult to understand for someone outside the field of data science. A CRV consists of the true positive rate or the percentage of positives correctly classified on the Y-axis and the percentage of the population targeted on the X-axis. It is important to note that the percentage of the population will be ranked by the model in descending order (either the probabilities or the expected values). If the model is good, then by targeting a top portion of the ranked list, all high percentages of positives will be captured.
Nowhere could the application of machine learning prove more important -- nor more risky -- than in law enforcement and national security. In this article, I'll review this area and then cover six perplexing and pressing ethical quandaries that arise. Predictive policing introduces a scientific element to law enforcement decisions, such as whether to investigate or detain, how long to sentence, and whether to parole. In making such decisions, judges and officers take into consideration the probability a suspect or defendant will be convicted for a crime in the future -- which is commonly the dependent variable for a predictive policing model. These independent variables may include prior convictions, income level, employment status, family background, neighborhood, education level, and the behavior of family and friends.