mhjensen
196 lines
7.8 KiB
HTML
196 lines
7.8 KiB
HTML
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<!-- navigation toc: --> <li><a href="._DimRed-bs001.html#___sec0" style="font-size: 80%;">Reducing the number of degrees of freedom, overarching view</a></li>
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<!-- navigation toc: --> <li><a href="#___sec1" style="font-size: 80%;">Preprocessing our data</a></li>
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<!-- navigation toc: --> <li><a href="._DimRed-bs012.html#___sec11" style="font-size: 80%;">Other techniques</a></li>
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<h2 id="___sec1" class="anchor">Preprocessing our data </h2>
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<p>
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Before we proceed however, we will discuss how to preprocess our
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data. Till now and in connection with our previous examples we have not met so many cases
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where we are too sensitive to the scaling of our data. Normally the
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data may need a rescaling and/or may be sensitive to extreme
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values. Scaling the data renders our inputs much more suitable for the
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algorithms we want to employ.
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<p>
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<b>Scikit-Learn</b> has several functions which allow us to rescale the data, normally resulting in much better results in terms of various accuracy scores. The <b>StandardScaler</b> function in <b>Scikit-Learn</b> ensures that for each feature/predictor we study the mean value is zero and the variance is zero (every column in the design/feature matrix).
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This scaling has the drawback that it does not ensure that we have a particular maximum or minumum in our data set. Another function included in <b>Scikit-Learn</b> is the <b>MinMaxScaler</b> which ensures that all features are exactly between \( 0 \) and \( 1 \). The <b>Normalizer</b> function scales each column of the design matrix by its Euclidean norm.
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