April 2016
Beginner to intermediate
384 pages
8h 36m
English
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Extracting the useful dimensions using Linear Discriminant Analysis
Now that we understand the mechanics (and trade-offs) of dimensionality reduction, let's use it for classification.
In this recipe, we will introduce Linear Discriminant Analysis (LDA). LDA, in contrast to the methods presented earlier in this chapter, aims at representing the dependent variable as a linear function of many other features; in that sense, it is similar to a regression (which we will discuss in the next chapter). The LDA shows similarities to the ANOVA analysis of variance and logistic regression in how it models the linear relationships in the data that capture (explain) the variance the best.
We will use a linear SVM classifier to test the effectiveness of our dimensionality ...
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I'm always learning. So when I got on to O'Reilly, I was like a kid in a candy store. There are playlists. There are answers. There's on-demand training. It's worth its weight in gold, in terms of what it allows me to do.