 Applying AI, recommendation or machine learning techniques to search feature
By : Phạm Linh
Date : March 29 2020, 07:55 AM
it helps some times This looks more like an optimization problem. You have some hard constraints and some preferences. Look at Linear Programming. Also google Constraint based Scheduling, there are several tutorials. Just a warning: This is in general an NP-hard problem, so unless you are trying to solve it for a small number participants, you will need to use some heuristics and approximations. If you want to go a little bit overboard, there is a coursera class on optimization running right now.
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Using Text Sentiment as feature in machine learning model?
By : Erased
Date : March 29 2020, 07:55 AM
I think the issue was by ths following , Of course, you can convert text input single number with sentiment analysis then use this number as a feature in your machine learning model. Nothing wrong with this approach.
The question is what kind of information you want to extract from text data. Because sentiment analysis convert text input to a number between -1 to 1 and the number represents how positive or negative the text is. For example, you may want sentiment information of the customers' comments about a restaurant to measure their satisfaction. In this case, it is fine to use sentiment analysis to preprocess text data.
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Feature order when used with a trained machine learning model
By : KGTTEN
Date : March 29 2020, 07:55 AM
will help you For simplicity assume that you are fitting a linear model and a regression model (but generalizes to all the others). If F1, F2, F3 are your features then it finds the weights w1, w2, w3, bias such that the error made by w1*F1 + w2*F2 + w3*F3 + bias is minimum. It is called the linear combination of weight and features.
So when making the prediction the model calcualtes the value w1*F1 + w2*F2 + w3*F3 + bias so the order of features matter.
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Machine learning model with relative feature importance
By : user2915528
Date : March 29 2020, 07:55 AM
it fixes the issue If I understand the question, you want to have some features be more important than others. To do this, you can assign weights to the individual features themselves based on which you want to be taken into account more heavily.
This question is rather broad so I hope this can be of help.
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What is the best way to use golden feature in machine learning model?
By : 余程洋
Date : October 07 2020, 03:00 PM
it fixes the issue You can have an ensemble modelling approach. Here is what it would look like: code :
feature_1 ---- | |
feature_2 ---- | Model 1 | ---- output 1
feature_n ---- | |
feature_1 ---- | |
feature_2 ---- | Model 2 | ---- output 2
feature_n ---- | |
feature_1 ---- | |
feature_2 ---- | Model n | ---- output n
feature_n ---- | |
output_1 ---- | |
output_2 ---- | Meta Model | ---- Final Classification
output_n ---- | |
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» 
code :
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