pca

Principal Component Analysis Implement from scratch and validate with sklearn framework Introduction : “Excess of EveryThing is Bad” The above line is specially in machine learning. When the data becomes too much in its dimension then it becomes a problem for pattern learning. Too much information is bad on two things : compute and execution time and quality of the model fit. When the dimension of the data is too high we need to find a way to reduce it. But that reduction has to be done in such a way that we maintain the original pattern of the data.  The algorithm that we are going to discuss in this article does the similar job. The algorithm is quite famous and widely used in varieties of tasks. Its name is Principal Component Analysis aks PCA. The main purposes of a principal component...

Speeding Up and Benchmarking Logistic Regression With PCA Introduction : When the data becomes too much in its dimension then it becomes a problem for pattern learning. Too much information is bad on two things : compute and execution time and quality of the model fit. When the dimension of the data is too high we need to find a way to reduce it. But that reduction has to be done in such a way that we maintain the original pattern of the data.  The algorithm that we are going to discuss in this article does the similar job. The algorithm is quite famous and widely used in varieties of tasks. Its name is Principal Component Analysis aka PCA. The main purpose of principal component analysis is the analysis of data to identify patterns and finding patterns to reduce the dimensions of the data...