## What preprocessing.scale() do? How does it work?

standardscaler vs minmaxscaler

python standardscaler example

sklearn preprocessing standardscaler

preprocessing scale vs standardscaler

sklearn preprocessing minmaxscaler

maxabsscaler

preprocessing.scale inverse

Python 3.5, preprocessing from sklearn

df = quandl.get('WIKI/GOOGL') X = np.array(df) X = preprocessing.scale(X)

The preprocessing.scale() algorithm puts your data on one scale. This is helpful with largely sparse datasets. In simple words, your data is vastly spread out. For example the values of X maybe like so:

X = [1, 4, 400, 10000, 100000]

The issue with sparsity is that it very biased or in statistical terms skewed. So, therefore, scaling the data brings all your values onto one scale eliminating the sparsity. In regards to know how it works in mathematical detail, this follows the same concept of Normalization and Standardization. You can do research on those to find out how it works in detail. But to make life simpler the sklearn algorithm does everything for you !

**sklearn.preprocessing.scale,** sklearn.preprocessing. scale (X, *, axis=0, with_mean=True, with_std=True, This implementation will refuse to center scipy.sparse matrices since it would make will be performed on the features of the CSC matrix) or to call X.toarray() if Python 3.5, preprocessing from sklearn df = quandl.get('WIKI/GOOGL') X = np.array(df) X = preprocessing.scale(X)

Scaling the data brings all your values onto one scale eliminating the sparsity and it follows the same concept of Normalization and Standardization. To see the effect, you can call describe on the dataframe before and after processing:

df.describe() #with X is already pre-proccessed df2 = pandas.DataFrame(X) df2.describe()

You will see df2 has **0 mean** and the standard **variation of 1** in each field.

**6.3. Preprocessing data,** An alternative standardization is scaling features to lie between a given minimum Any other sparse input will be converted to the Compressed Sparse Rows representation. scaling using the mean and variance of the data is likely to not work very well. Normalizer().fit(X) # fit does nothing >>> normalizer Normalizer(). Preprocessing in Data Science (Part 1): Centering, Scaling, and KNN Data preprocessing is an umbrella term that covers an array of operations data scientists will use to get their data into a form more appropriate for what they want to do with it.

preprocessing.scale() method is helpful in standardization of data points. It would divide by the standard deviation and substract the mean for each data point.

**Scale, Standardize, or Normalize with Scikit-Learn,** This guide is current as of scikit-learn v0.20.3. What do These Terms Mean? Scale generally means to change the range of the values. The shape class sklearn.preprocessing. StandardScaler(*, copy=True, with_mean=True, with_std=True) [source] ¶. Standardize features by removing the mean and scaling to unit variance. The standard score of a sample x is calculated as: z = (x - u) / s. where u is the mean of the training samples or zero if with_mean=False , and s is the standard deviation of the training samples or one if with_std=False.

**Preprocessing with sklearn: a complete and comprehensive guide,** Sklearn its preprocessing library forms a solid foundation to guide you… After the deletion of our second sample, f2 did not have missing values anymore. (ffill) or fill backward (bfill), which are convenient when working with time series. StandardScaler()scaler.fit_transform(X.f3.values.reshape(-1, 1)). If not an exception, then I'd expect the preprocessing.scale () function to deal with non-finite values in some way - perhaps ignoring them in the array when scaling, and potentially returning a warning at the same time. This comment has been minimized. Sign in to view. Copy link.

**sklearn.preprocessing.scale Python Example,** This page provides Python code examples for sklearn.preprocessing.scale. The following are code examples for showing how to use sklearn.preprocessing.scale(). columns=for_pca_df.columns) # Run PCA self.num_components data = scale(data) # The reconstruction error will decrease as n_components is Scales -- or, more specifically, balances -- weighed heavily on the minds of ancient builders, inventors and economic advisers. Small balance weights dating back to the early fourth millennium B.C. provide some of the first hints of mankind's evolving grasp of numbers.

**What preprocessing.scale() do? How does it work? – Icetutor,** The preprocessing.scale() algorithm puts your data on one scale. This is helpful with largely sparse datasets. In simple words, your data is Data preprocessing is a data mining technique that involves transforming raw data into an understandable format. Real-world data is often incomplete, inconsistent, and/or lacking in certain behaviors or trends, and is likely to contain many errors. Data preprocessing is a proven method of resolving such issues.

##### Comments

- Have you looked at the documentation?
- yeah but I can't understand what it is doing to the values of X ?
- I beleive it subtracts the mean and divides by the standard deviation of your dataset along a given axis.
- here is another link this can help.
- After scaling this data will still be skewed. It will just be a lot closer to zero. Also an array of numbers cannot be biased unless there is some ground truth this is trying to represent.
- A bit misleading, in that it would subtract the mean of your points first, then divide by the standard deviation. Alternatively you can divide by the standard deviation, compute the new mean, and subtract that.