23
votes
Are there any tools for feature engineering?
Very interesting question (+1). While I am not aware of any software tools that currently offer comprehensive functionality for feature engineering, there is definitely a wide range of options in that ...
- 6,518
13
votes
List of feature engineering techniques
Missing Data Imputation:
Complete case analysis
Mean / Median / Mode imputation
Random Sample Imputation
Replacement by Arbitrary Value
Missing Value Indicator
Multivariate imputation
...
- 251
10
votes
List of feature engineering techniques
There is no definite source on how to do feature engineering. It is often dependent on the problem you are trying to solve. Some say it is more of an art than it is science.
But I would go through ...
- 718
9
votes
Accepted
Combining Latitude/Longitude position into single feature
A note: for those who've ended here looking for a hashing technique, geohash is likely your best choice.
Representing latitude and longitude in a single linear scale is not possible due to the fact ...
8
votes
How to combine categorical and continuous input features for neural network training
There's three main approaches to solving this:
Building two models separately and then training an ensemble algorithm that receives the output of the two models as an input
Concating all the data ...
- 181
8
votes
Are there any tools for feature engineering?
Featuretools is a recently released python library for automated feature engineering. It's based on an algorithm called Deep Feature Synthesis originally developed in 2015 MIT and tested on public ...
- 181
8
votes
How to deal with categorical feature of very high cardinality?
This is an old question. I am surprised that I don't see anyone mentioned Mean Encoding (a.k.a Target Encoding). It is very popular in supervised learning problems. Besides, I have seen people use ...
- 181
7
votes
Accepted
How to deal with categorical feature of very high cardinality?
One-hot-encoded ZIP codes shouldn't present a problem with modern tools, where features can be much wider (millions, billions even), but if you really want you could aggregate area codes into regions, ...
- 10.5k
7
votes
Is this a good practice of feature engineering?
1) Yes, it makes sense. Trying to create features manually will help the learners (i.e. models) to graspe more information from the raw data because the raw data is not always in a form that is ...
- 71
6
votes
Accepted
What to do when testing data has less features than training data?
Use the extra features for unsupervised learning. You might enjoy Vladimir Vapnik's take on this in the context of SVMs, which he calls privileged learning: Learning with Intelligent Teacher: ...
- 10.5k
6
votes
Accepted
Is this a good practice of feature engineering?
If you can keep adding new data (based on a main concept such as area i.e. the ZIP code) and the performance of your model improves, then it is of course allowed... assuming you only care about the ...
- 14.6k
5
votes
Accepted
Convolutional Neural networks
Why don't we convolve our images against the last convolution layer and see how many of these complex feature filters get activated?
The answer is that all the layers are fully dependent on the exact ...
- 28.3k
5
votes
Accepted
How to transform raw data to fixed-frequency time series?
This sort of effect can be achieved with
pandas.DataFrame.resample() combined with Resampler.aggregate() like:
Code:
...
- 1,773
4
votes
Accepted
Is there any difference between feature extraction and feature learning?
Yes I think so. Just by looking at Feature Learning and Feature extraction you can see it's a different problem.
Feature extraction is just transforming your raw data into a sequence of feature ...
- 211
4
votes
Accepted
Is it a good idea to train with a feature which value will be fixed in future predictions?
Given that in your training data this feature has different values and some predictive power, I think not keeping this feature would be a mistake (without looking into overfitting due to having too ...
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4
votes
Is this a good practice of feature engineering?
Usually, the richer the features the better.
One thing to keep in mind, however, regressions, in general, do not work well with data that is highly correlated (multicollinearity). When you expand ...
- 474
4
votes
Too much inputs = overfitting?
Yes, you can mix any different sort of inputs when the scales of the features are similar, which is achieved by normalising the feature vectors.
I assume you mean too many features when you say 'too ...
- 205
4
votes
What are features for state-action pairs in RL?
A feature vector is a vector that is containing basis functions. These basis functions are combining states and actions. We can use a feature vector to approximate our action-value function $q(\...
- 1,918
3
votes
Approach to creating a user profile in music web application
The first question is: what to do you want to see in user profile?
Top-10 tracks, top-10 artist by user?
How many tracks/artists a user listens to in a day on average (may be, in last month)?
May be ...
- 5,434
3
votes
How to deal with categorical feature of very high cardinality?
You can use embedding which is mentioned in the comments. e.g. A general blog post, Keras documentation for embedding layer which can be used to learn the embedding. This is widely used by deep ...
- 131
3
votes
Accepted
how to evaluate feature quality for decision tree model
The main reasons for seeking an efficient feature selection are the machine learning algorithm get faster training, reduces the complexity of a model, facilitates interpretation and improves the ...
- 46
3
votes
Accepted
What are features for state-action pairs in RL?
In the cartpole example, a state-action feature could be
$$\begin{bmatrix}
\text{Cart Position}\\
\text{Cart Velocity}\\
\text{Pole Angle}\\
\text{Pole Tip Velocity}\\
\text{Action}
\end{bmatrix}$$
...
3
votes
Combining Latitude/Longitude position into single feature
The best practice is to not attempt to flatten Earth into a onee dimensional line... Because as you may know, Earth more resembles a sphere than a line. It is much better to treat it as such properly.
...
- 7,949
3
votes
Finding if an outcome is predictable
That would be a part of feature selection. There are many methods to find out if there are relationships between the dependent variable and independent variables. To name a few: plots, measures of ...
- 115
2
votes
Are there any tools for feature engineering?
Scikit-learn has recently released new transformers that tackle many aspects of feature engineering. For example:
You can do multiple missing data imputation techniques with the ...
- 251
2
votes
What to do when testing data has less features than training data?
I think there might be a problem in the way you are stating the problem. You say that you test data doesn't have two fields, but that can not be correct.
You have to take all your data and split it ...
- 901
2
votes
Approach to creating a user profile in music web application
You can download a free as in beer software Qlikview that allows you to do interactive data discovery via graphical interface similar to Excel but also featuring a powerful scripting language for data ...
- 550
2
votes
Are there any tools for feature engineering?
Feature Engineering is at the heart of Machine Learning and is rather laborious and time consuming. There have been various attempts at automating feature engineering in hopes of taking the human out ...
- 1,605
2
votes
Accepted
Time-stamp for linear model
Welcome to Datascience.SE!
Like you said, you can extract the day of the week. Also extract the hour of the day, then encode these two variables using sines and cosines with their respective ...
- 10.5k
2
votes
Accepted
Why adding combinations of features would increase performance of linear SVM?
Multiplication is not a linear operation. Your linear SVM constructs a (hyper-)plane
$$
w_0 = w_1 x_1 + w_2 x_2
$$
for some weights $w_0, w_1, w_2.$
By introducing the AND-feature, you add another ...
- 1,636
Only top scored, non community-wiki answers of a minimum length are eligible