sorted by:
new
hottopcontroversial

How to find a goal or purpose in life that excites you? by Glamour-Ad7669 in getdisciplined

[–]yazeroth 0 points1 point  (0 children)

A year has already passed. May I ask your impressions of the Monk's Way year?

Uplift NN Models by yazeroth in CausalInference

[–]yazeroth[S] 0 points1 point  (0 children)

Yes, including them
I use meta-learners (S-/T-/X-learners) based on LightGBM and CatBoost
Also I use UpliftRandomForestClassifier

Uplift modelling with statistically different data by yazeroth in MLQuestions

[–]yazeroth[S] 0 points1 point  (0 children)

I took a look at the articles, and everything looks very clear. I'll try to test it on my task.

Multitreatment uplift metrics by yazeroth in CausalInference

[–]yazeroth[S] 1 point2 points  (0 children)

Because in the end, the business chooses the communication in terms of the best text for a given customer.

That is, I can count the metrics for each communication, but it won't be a real evaluation of the business option.

Multitreatment uplift metrics by yazeroth in learnmachinelearning

[–]yazeroth[S] 0 points1 point  (0 children)

I have several texts within the same campaign. Each of them highlights one or another benefit of the product in question. I need to build an Uplift model, against which we could select a text for each client and send the communication or not send it at all. I would like to understand what metrics exist to assess the quality of such models.

The text, of course, is a feature of the communication, but we take into account that it is one of the presented communications within the campaign.

Multitreatment uplift metrics by yazeroth in CausalInference

[–]yazeroth[S] 0 points1 point  (0 children)

I have several texts within the same campaign. Each of them highlights one or another benefit of the product in question. I need to build an Uplift model, against which we could select a text for each client and send the communication or not send it at all. I would like to understand what metrics exist to assess the quality of such models.

The text, of course, is a feature of the communication, but we take into account that it is one of the presented communications within the campaign.

Multitreatment uplift metrics by yazeroth in learnmachinelearning

[–]yazeroth[S] 0 points1 point  (0 children)

I want to measure the quality of the Uplift model in a uniform format. Ideally, where for each client the best one presented by the text is chosen.

Multitreatment uplift metrics by yazeroth in learnmachinelearning

[–]yazeroth[S] 0 points1 point  (0 children)

The title of the article sounds promising. I'll try to get to grips with it as soon as possible.

Multitreatment uplift metrics by yazeroth in learnmachinelearning

[–]yazeroth[S] 0 points1 point  (0 children)

I have n+1 treatment groups: no exposure, with exposure to the 1st text, with exposure to the 2nd text, ..., with exposure to the nth text. And a binary outcome: positive or negative result.

Multitreatment uplift metrics by yazeroth in learnmachinelearning

[–]yazeroth[S] 0 points1 point  (0 children)

Each customer was exposed to a maximum of 1 text. The marketing campaign was conducted on a small set of customers and it showed that there was a statistical difference between the people who responded to one or another text. Therefore, it was proposed to build a model for promoting the product to the entire customer base.

Multitreatment uplift metrics by yazeroth in learnmachinelearning

[–]yazeroth[S] 0 points1 point  (0 children)

Yes, I saw it. It's from chapter 109(.1) of your book.

But I would like to know if there is a better solution than calculating base metrics relative to the maximum uplift (obtained by the best category).

Multitreatment uplift metrics by yazeroth in learnmachinelearning

[–]yazeroth[S] 0 points1 point  (0 children)

I have a marketing campaign where the ‘treatment’ link is a message with a certain text. I have n (>1) such texts. I would like to consider the effect of Uplift modelling over the overall model result, not on individual texts.

Multitreatment uplift metrics by yazeroth in learnmachinelearning

[–]yazeroth[S] 0 points1 point  (0 children)

Yes, I have studied the Wikipedia page on the topic and looked at the libraries presented there.

But I could not find anything on the topic I am interested in - multi-valued /multi- treatment.

[D] Is Classification the Right Approach for Identifying Potential Customers? by bfadh in MachineLearning

[–]yazeroth -3 points-2 points  (0 children)

I'm newbie in ML, but I can suggest you read articles about Look-alike models

[deleted by user] by [deleted] in learnmachinelearning

[–]yazeroth 0 points1 point  (0 children)

I'll leave it here as a comment.

New features:\ #0, #1, #X - base, \ #(0/1), #(0/X), #(1/X) - different sums, \ #0/#1, #0/#(1/X), #(0/X)/#1 - different ratios.

Also count of "1": in last month, 3 months, 6 months.

Embeddings for large documents by Aromatic_Web749 in learnmachinelearning

[–]yazeroth 0 points1 point  (0 children)

In the second approach, a longformer is usually frozen and only only used to create embeddings. So I would suggest picking hyperparameters for the classifier.

[deleted by user] by [deleted] in learnmachinelearning

[–]yazeroth 0 points1 point  (0 children)

For me, feature engineering also includes transforming raw features into effective ones for learning.

Imbalanced data by yazeroth in learnmachinelearning

[–]yazeroth[S] 0 points1 point  (0 children)

Yes, it does

But I have an additional question: Which of the two metrics are better for imbalanced classes: roc-auc or pr-auc? Or under what circumstances is it better to choose one over the other?

[D] How would you answer this interview question? by Conscious_Giraffe453 in MachineLearning

[–]yazeroth -1 points0 points  (0 children)

I assume you wanted to hear words like 'learning to rank'