This is conditioning in the sense of conditional probability. The idea is that the authors have some "standard physically-inspired features". They are splitting the data up into bins based on the values of these features, and then training a model for each bin. They are then examining the differences between the models. Usually this is done to learn ...
manual feature engineering started becoming obsolete
That is wrong.
Any suggestion on when to use manual feature engineering, feature learning or a combination of the two?
Deep learning is awesome for natural signals like images, audio or large amounts of unstructured text (e.g. arbitrary crawled websites)
There are some basic steps that make almost ...
I answered a similar question earlier and here is a piece of my answer that i think covers your question:
Batch normalization's assistance to neural networks wasn't really understood for the longest time, initially it was thought to assist with internal covariate shift (hypothesized by the initial paper: Batch Normalization: Accelerating Deep Network ...
Here's a list of some of the best python libraries for natural language processing.
Natural Language Toolkit (nltk)
Covers all the basic functions and NLP tools such as tokenization etc.
This is a good library of beginners, it provides the nltk toolkit in a simplified format.
It is an advanced library and can be used in production code.
Data preprocessing consists of all those techniques used to generate the final datasets (with an appropriate size, structure, and format) for the machine learning algorithms or models. Data acquisition should not be part of data preprocessing, but the step preceding it, which gathers the raw data (which may e.g. be noisy).
The book Data Preprocessing in ...
You can use append function:
final = df1.append(df2, ignore_index=True)
To set the last column as labels, you set them as so by:
labels = np.array(final["will_buy"])
So, when calling the fit method on the model you build, you set labels = labels.
In general, algorithms that exploit distances or similarities (e.g. in the form of scalar product) between data samples, such as k-NN and SVM, are sensitive to feature transformations. We do feature scaling to make our model robust to outliers and make an initial impact of every feature on the model will be roughly similar
Graphical-model based classifiers, ...
No, there is no computational advantage of the second method over the first, if you neglect the computational requirements for the calculation of $\sigma$ and $\mu$.
We generally use the first method for better results. This is because if you separate your dataset into train and test data, then you may normalise the train data perfectly between $0$ and $1$ ...