I am pretty new to machine learning and I am currently dealing with a dataset in the format of a csv file comprised of categorical data. As a means of preprocessing, I One Hot Encoded all the variables in my dataset.
At the moment I am trying to apply a random forest algorithm to classify the entries into one of the 4 classes. My problem is that I do not understand exactly what happens to these One Hot Encoded variables. How do I feed them to the algorithm? Is it able to make the difference between buying_price_high, buying_price_low (One Hot Encoded from buying_price)?
I One Hot Encoded the response variable as well.
Method of (One Hot Encoder) applies to category variables, and category variables have no size relationship.For the price variable,I suggest you use OrinalEncoder.Sklearn is a good package for machine.like, sklearn learning.preprocessing.OneHotEncoder or sklearn.preprocessing.OrdinalEncoder
I guess you're having problem understanding One Hot Encoder. Lets suppose you've 4 classes what one hot encoder will do it will convert those labels into binary numbers whereas LabelEncoder will give them labels as 0,1,2,3 and so on. It is better to use One Hot encoder because ML models will give higher weightage to label 3 than label 2.
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I'm attempting to use sklearn's linear regression model to predict fantasy players points. I have numeric stats for each player and obviously their name which I have encoded with the Label encoder function. My question is when performing the linear regression the encoded values included in the training it doesn't seem to recognize it as an ID but instead treats it as a numeric value.
So is there a better way to encode player names so they are treated as an ID so that it recognizes player 1 averages 25 points compared to player 2's 20? Or is this type of encoding even possible with linear regression? Thanks in advance
Apart from one hot encoding (which might create way too many columns in this case), mean target encoding does exactly what you need (encodes the category with its mean target value). You should be vary about the target leakage in case of rare categories though. sklearn-compatible category_encoders library provides several robust implementations, such as LeaveOneOutEncoder()
I have a question regarding random forests. Imagine that I have data on users interacting with items. The number of items is large, around 10 000. My output of the random forest should be the items that the user is likely to interact with (like a recommender system). For any user, I want to use a feature that describes the items that the user has interacted with in the past. However, mapping the categorical product feature as a one-hot encoding seems very memory inefficient as a user interacts with no more than a couple of hundred of the items at most, and sometimes as little as 5.
How would you go about constructing a random forest when one of the input features is a categorical variable with ~10 000 possible values and the output is a categorical variable with ~10 000 possible values? Should I use CatBoost with the features as categorical? Or should I use one-hot encoding, and if so, do you think XGBoost or CatBoost does better?
You could also try entity embeddings to reduce hundreds of boolean features into vectors of small dimension.
It is similar to word embedings for categorical features. In practical terms you define an embedding of your discrete space of features into a vector space of low dimension. It can enhance your results and save on memory. The downside is that you do need to train a neural network model to define the embedding before hand.
Check this article for more information.
XGBoost doesn't support categorical features directly, you need to do the preprocessing to use it with catfeatures. For example, you could do one-hot encoding. One-hot encoding usually works well if there are some frequent values of your cat feature.
CatBoost does have categorical features support - both, one-hot encoding and calculation of different statistics on categorical features. To use one-hot encoding you need to enable it with one_hot_max_size parameter, by default statistics are calculated. Statistics usually work better for categorical features with many values.
Assuming you have enough domain expertise, you could create a new categorical column from existing column.
ex:-
if you column has below values
A,B,C,D,E,F,G,H
if you are aware that A,B,C are similar D,E,F are similar and G,H are similar
your new column would be
Z,Z,Z,Y,Y,Y,X,X.
In your random forest model you should removing previous column and only include this new column. By transforming your features like this you would loose explainability of your mode.
I am using an MLP Classifier. There are around 4-5 categorical attributes (such as Gender(Male/Female), Smoking(Yes/No), Diabetes(Yes/No), Hypertension(Yes/No)).
Do I necessarily need to use one hot encoding on all these features before using a neural network classifier? I don't have a lot of training data (only 130 samples).
Can I just get away with Label Encoding these attributes?
Of course, that will be enough. There is no gain in using one hot in this case.
My dataset has few features with yes/no (categorical data). Few of the machine learning algorithms that I am using, in python, do not handle categorical data directly. I know how to convert yes/no, to 0/1, but my question is -
Is this a right approach to go about it?
Can these values of no/yes to 0/1, be misinterpreted by algorithms ?
The algorithms I am planning to use for my dataset are - Decision Trees (DT), Random Forests (RF) and Neural Networks (NN).
Yes, in my opinion, encoding yes/no to 1/0 would be the right approach for you.
Python's sklearn requires features in numerical arrays.
There are various ways of encoding : Label Encoder; One Hot Encoder. etc
However, since your variable only has 2 levels of categories, it wouldnt make much difference if you go for LabelEncoder or OneHotEncoder.
I wish to fit a logistic regression model with a set of parameters. The parameters that I have include three distinct types of data:
Binary data [0,1]
Categorical data which has been encoded to integers [0,1,2,3,...]
Continuous data
I have two questions regarding pre-processing the parameter data before fitting a regression model:
For the categorical data, I've seen two ways to handle this. The first method is to use a one hot encoder, thus giving a new parameter for each category. The second method, is to just encode the categories with integers within a single parameter variable [0,1,2,3,4,...]. I understand that using a one hot encoder creates more parameters and therefore increases the risk of over-fitting the model; however, other than that, are there any reasons to prefer one method over the other?
I would like to normalize the parameter data to account for the large differences between the continuous and binary data. Is it generally acceptable to normalize the binary and categorical data? Should I normalize the categorical and the continuous parameters but not the binary parameters or can I just normalize all the parameter data types.
I realize I could fit this data with a random forest model and not have to worry much about pre-processing, but I'm curious how this applies with a regression type model.
Thank you in advance for your time and consideration.