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Which comes first - domain expertise or an experimental approach?

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9 votes

1 answer

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Why regularization parameter called as lambda in theory and alpha in python?

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asked Feb 11, 2021 at 3:24

9 votes

1 answer

436 views

How to calibrate models if we don't have enough data?

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asked Apr 2 at 10:02

5 votes

2 answers

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Interpreting table 1 in clinical research papers

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4 votes

3 answers

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Why significant variable doesn't improve model performance?

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asked Jan 1, 2020 at 7:09

4 votes

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Why and how to match variables using logistic regression?

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4 votes

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3 votes

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asked May 18 at 10:11

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3 votes

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How to adjust confounders in Logistic regression?

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Does low frequent items require data preparation as high freq items?

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asked Jun 13, 2020 at 13:55

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Why does my model produce unrealistic output?

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how is confounders different from interaction terms?

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asked Dec 30, 2019 at 10:20

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How to select best feature set?

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When to drop correlated features?

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asked Jan 6, 2020 at 0:23

2 votes

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How to know if I have enough controllable info for ML modelling?

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likelihood vs confidence in layman terms

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asked Jan 17 at 13:07

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Imbalanced classification vs anomaly detection

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Generating interesting/useful features from existing useless features

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How to interpret feature hashing ouptut?

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Using outcome label to create feature qualifies as data leakage?

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123 Questions

Which comes first - domain expertise or an experimental approach?

Why regularization parameter called as lambda in theory and alpha in python?

How to calibrate models if we don't have enough data?

Interpreting table 1 in clinical research papers

Why significant variable doesn't improve model performance?

Why and how to match variables using logistic regression?

Why use supervised binning on train data if it leaks data?

Rule based label - random split vs time-based split

What is the use of expected value in machine learning models?

Why ML involves trial and error and no overarching rules?

Usefulness of KS tests and other similar distribution comparing tests

MOOC on Causal analysis - Recommendations for software engineer

Understanding the 10:1 events per variable rule

How to adjust confounders in Logistic regression?

Does low frequent items require data preparation as high freq items?

Plotting categorical data along with numerical variable

What is noise-tolerant learning?

Why does my model produce unrealistic output?

How to make a decision - when there is a tie and no human expert

how to calculate median follow-up time?

how is confounders different from interaction terms?

How to select best feature set?

When to drop correlated features?

How to know if I have enough controllable info for ML modelling?

likelihood vs confidence in layman terms

Imbalanced classification vs anomaly detection

Generating interesting/useful features from existing useless features

How to interpret feature hashing ouptut?

Using outcome label to create feature qualifies as data leakage?

1D cluster - Jenks optimization - Finding optimal number