MLAI
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Difference PCA and Factor analysisMLAI/DimensionalityReduction 2020. 1. 23. 11:06
1. Overview Both are dimension reduction techniques, but while Principal Component Analysis is used to reduce the number of variables by creating principal components, extracting the essence of the dataset in the means of artificially created variables, which best describe the variance of the data. Factor Analysis tries to identify, unknown latent variables to explain the original data. Often pr..
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Natural Language Processing (NLP)MLAI 2020. 1. 22. 23:32
1. Overview Natural language process (NLP) is an area of Computer Science and Artificial Intelligence concerned with interactions between computers and human or natural languages. NLP is used to apply machine learning models to text and language. 2. Description 2.1 Bag of Words Very popular NLP model. It is a model used to preprocess the texts to classify before fitting the classification algori..
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Thompson SamplingMLAI 2020. 1. 22. 08:31
1. Overview 2. Description 2.1 Intuition when you get a job description but basically just imagine distribution behind each one of these expected values. So this is just the center of central distribution or the actual expected return from that machine. the algorithm actually works the algorithm itself doesn't know this information. So this is hidden but it's just there for us so that we can bet..
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EclatMLAI 2020. 1. 22. 07:31
1. Overview 2. Description 2.1 Supports In the Eclat model just like in the a priori model, we have the support factor. So people who are watching a certain combinations of movies. So we're just looking at how frequently does this set of items occur. How often does this set off to movies that say interstellar and ex-machine? How often does it occur in all of the watch lists or what percentage of..
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Hierarchical Clustering and DendrogramsMLAI/Clustering 2020. 1. 22. 06:17
1. Overview If you have points on your scatterplot or data points as we looked at previously this is a two-dimensional space. If you apply a hierarchical clustering or just say H.C. for short. What'll happen is you will get clusters again very very similar to Kamins In fact sometimes the result of no results can be exactly the same as like k-means clustering. But the whole process is a bit diffe..
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Decision TreeMLAI/Classification 2020. 1. 21. 16:55
1. Overview A decision tree is a decision support tool that uses a tree-like model of decisions and their possible consequences, including chance event outcomes, resource costs, and utility. It is one way to display an algorithm that only contains conditional control statements. Decision trees are commonly used in operations research, specifically in decision analysis, to help identify a strateg..
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Kernel SVMMLAI/Classification 2020. 1. 21. 00:20
1. Overview 2. Description 2.1 Motivation This happens because in this case the data is not linearly set separable. 2.2 Mapping to a Higher Dimension And now what we want to do is we just want to see that it is indeed linearly separable. And as you can see this dataset became linearly separable in this dimension. It is possible to do the same thing applies to two-dimensional space moving into th..
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Support Vector Machine (SVM)MLAI/Classification 2020. 1. 20. 21:58
1. Overview 2. Description 2.1 Components The line that separates these two classes of points. And at the same time, it has the maximum margin which means this distance so this line is drawn equidistant. And that's margin's So the sum of these two distances has to be maximized in order for this line to be the result of the SVM. And these two points are actually called the support vectors. So bas..