data-mining

What information can we access from the client? [closed]

by

Here is most of the information: var info={ timeOpened:new Date(), timezone:(new Date()).getTimezoneOffset()/60, pageon(){return window.location.pathname}, referrer(){return document.referrer}, previousSites(){return history.length}, browserName(){return navigator.appName}, browserEngine(){return navigator.product}, browserVersion1a(){return navigator.appVersion}, browserVersion1b(){return navigator.userAgent}, browserLanguage(){return navigator.language}, browserOnline(){return navigator.onLine}, browserPlatform(){return navigator.platform}, javaEnabled(){return navigator.javaEnabled()}, dataCookiesEnabled(){return navigator.cookieEnabled}, dataCookies1(){return document.cookie}, dataCookies2(){return decodeURIComponent(document.cookie.split(“;”))}, dataStorage(){return localStorage}, sizeScreenW(){return screen.width}, sizeScreenH(){return screen.height}, sizeDocW(){return document.width}, sizeDocH(){return document.height}, sizeInW(){return innerWidth}, sizeInH(){return innerHeight}, sizeAvailW(){return … Read more

What is the difference between Gradient Descent and Newton’s Gradient Descent?

by

At a local minimum (or maximum) x, the derivative of the target function f vanishes: f'(x) = 0 (assuming sufficient smoothness of f). Gradient descent tries to find such a minimum x by using information from the first derivative of f: It simply follows the steepest descent from the current point. This is like rolling … Read more

Mixing categorial and continuous data in Naive Bayes classifier using scikit-learn

by

You have at least two options: Transform all your data into a categorical representation by computing percentiles for each continuous variables and then binning the continuous variables using the percentiles as bin boundaries. For instance for the height of a person create the following bins: “very small”, “small”, “regular”, “big”, “very big” ensuring that each … Read more

What is an intuitive explanation of the Expectation Maximization technique? [closed]

by

Note: the code behind this answer can be found here. Suppose we have some data sampled from two different groups, red and blue: Here, we can see which data point belongs to the red or blue group. This makes it easy to find the parameters that characterise each group. For example, the mean of the … Read more

Why is the F-Measure a harmonic mean and not an arithmetic mean of the Precision and Recall measures?

by

To explain, consider for example, what the average of 30mph and 40mph is? if you drive for 1 hour at each speed, the average speed over the 2 hours is indeed the arithmetic average, 35mph. However if you drive for the same distance at each speed — say 10 miles — then the average speed … Read more

Why does one hot encoding improve machine learning performance? [closed]

by

Many learning algorithms either learn a single weight per feature, or they use distances between samples. The former is the case for linear models such as logistic regression, which are easy to explain. Suppose you have a dataset having only a single categorical feature “nationality”, with values “UK”, “French” and “US”. Assume, without loss of … Read more

How does the Amazon Recommendation feature work?

by

It is both an art and a science. Typical fields of study revolve around market basket analysis (also called affinity analysis) which is a subset of the field of data mining. Typical components in such a system include identification of primary driver items and the identification of affinity items (accessory upsell, cross sell). Keep in … Read more

Difference between classification and clustering in data mining? [closed]

by

In general, in classification you have a set of predefined classes and want to know which class a new object belongs to. Clustering tries to group a set of objects and find whether there is some relationship between the objects. In the context of machine learning, classification is supervised learning and clustering is unsupervised learning. … Read more

What is the difference between linear regression and logistic regression? [closed]

by

Linear regression output as probabilities It’s tempting to use the linear regression output as probabilities but it’s a mistake because the output can be negative, and greater than 1 whereas probability can not. As regression might actually produce probabilities that could be less than 0, or even bigger than 1, logistic regression was introduced. Source: … Read more

Hata!: SQLSTATE[HY000] [1045] Access denied for user 'divattrend_liink'@'localhost' (using password: YES)