Data Dimensionality Reduction in the Age of Machine Learning - DATAVERSITY

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Click to learn more about author Rosaria Silipo. Machine Learning is all the rage as companies try to make sense of the mountains of data they are collecting. Data is everywhere and proliferating at unprecedented speed. But, more data is not always better. In fact, large amounts of data can not only considerably slow down the system execution but can sometimes even produce worse performances in Data Analytics applications.


Seven Techniques for Data Dimensionality Reduction

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The recent explosion of data set size, in number of records and attributes, has triggered the development of a number of big data platforms as well as parallel data analytics algorithms. At the same time though, it has pushed for usage of data dimensionality reduction procedures. Indeed, more is not always better. Large amounts of data might sometimes produce worse performances in data analytics applications. One of my most recent projects happened to be about churn prediction and to use the 2009 KDD Challenge large data set.


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@machinelearnbot

This powerful quote by William Shakespeare applies well to techniques used in data science & analytics as well. Allow me to prove it using a short story. In May ' 2015, we conducted a Data Hackathon ( a data science competition) in Delhi-NCR, India. We gave participants the challenge to identify Human Activity Recognition Using Smartphones Data Set. The data set had 561 variables for training model used for the identification of Human activity in test data set.


Beginners Guide To Learn Dimension Reduction Techniques

@machinelearnbot

This powerful quote by William Shakespeare applies well to techniques used in data science & analytics as well. Allow me to prove it using a short story. In May ' 2015, we conducted a Data Hackathon ( a data science competition) in Delhi-NCR, India. We gave participants the challenge to identify Human Activity Recognition Using Smartphones Data Set. The data set had 561 variables for training model used for the identification of Human activity in test data set.


Feature Engineering using R

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To summarize, it's important to spend time extracting, selecting and constructing features based on your data and it's size. It will be valuable in improving the performance of your model. Some features could be selected from a given feature set based on correlation with other predictors or with the label. If you have a small set of features, a quick brute-force approach of attempting different combinations might give you the best set of predictors. If you have a huge feature set especially when compared to the total number of data points – exploring dimensionality reduction techniques or other methods to combine multiple features together might be of help. But ultimately, though some of the methods above would help pick features, it's up to you to evaluate their value in being good predictors.