6 votes

Why does data science see class imbalance as a problem for supervised learning when statistics does not?

It's generally not related to Data Science but what goes around; typically all sort of bad practice relating to laziness / looking for short term rewards. I wouldn't say DS is pushing for it but ...
Lucas Morin's user avatar
  • 2,093
4 votes

Why do we need hyperparameter tuning in Scikit learn? Doesn't sk learn models by default give best model?

You are wrong. RandomForestClassifier does not try any hyperparameters. You need to give it the specific value for each of its hyperparameters. Given such ...
noe's user avatar
  • 25.6k
3 votes

Is it valid changing the classification treshold of neural networks for improving the classification performance?

Yes it is a very common thing to do, for controlling tradeoff of objectives. One often encountered example is to precision-recall tradeoff where we move the threshold to strike a balance between ...
lpounng's user avatar
  • 998
3 votes
Accepted

How to fit n features in a number of neurons smaller than n

To reduce the number of features from the raw data to 64 input neurons, you can perform dimensionality reduction. There are various methods to do this, here are some common ones that are easy to ...
Leo's user avatar
  • 146
3 votes

Solve tough clustering problem with overlapping clusters

The Problem is that many clustering algorithms focus on distances (between points, clusters and so on). Especially at the connection between the two desired clusters, distances between points are ...
Broele's user avatar
  • 1,352
3 votes
Accepted

Xgboost model predicting extreme values for events and non-events | Overfitting

This is not necessarily overfitting, but it may indicate data leakage i.e you are passing information to the model that is not supposed to be there it may be: Information that is generated after the ...
Multivac's user avatar
  • 2,949
3 votes

Car Make and Model detection

For detecting the make and model of cars from images with high accuracy across a large number of classes, I would recommend a convolutional neural network (CNN) architecture tailored for fine-grained ...
Multivac's user avatar
  • 2,949
3 votes

ROC curve for a perfect model, why is AUC 1.0?

The difficulty with ROC curves is to understand what happens when the threshold varies. There is no summing, the curve only depends on how many instances have TP/FP/TN/FN status for every threshold. A ...
Erwan's user avatar
  • 25.2k
2 votes

Is deep learning high initial validation accuracy a sign of problem?

It's normal for the validation accuracy to start lower than the training accuracy, especially in the first few epochs. This is because the model is still learning and adjusting its weights to fit the ...
irazza's user avatar
  • 46
2 votes
Accepted

what qualifies as a data leakage?

Data leakage occurs in cases when you train a model with data that is not available for future testing/inference; or when you use same piece of data for training, and then for validation and/or ...
Stefan Popov's user avatar
2 votes

what qualifies as a data leakage?

One definition of data leakage is providing the model with data during training that would not be available at a future prediction time. The variable "total target achieved/units purchased as on ...
Brian Spiering's user avatar
2 votes
Accepted

Are imbalanced data problems solvable?

The issue is not with the imbalance per se. The issue is that your categories are not particularly separable on the available data (or they are but you are not modeling the correct relationship, e.g., ...
Dave's user avatar
  • 3,688
2 votes
Accepted

Different Algorithms for 50-50 A/B Testing

You can reformulate your previous even/odd split as bit testing of the binary representation of the customer ID: for the first feature, you took the bit at the first position (the least significant ...
noe's user avatar
  • 25.6k
2 votes

Random Forest Classification model performing much better with 70:30 TEST:TRAIN rather than the opposite

The results you are receving may be affected by variance. When you evaluate the model on the 30% of the data, you will have low bias but more variance. The imbalance of the target should not be a ...
Multivac's user avatar
  • 2,949
2 votes

How to intrepret low F1 score and high AUC on training set?

Your model is overfitting. This is evident from the high ROC AUC score on the training data and the lower F1 score on the validation data. The ROC AUC score is a measure of the model's ability to ...
Bayrem's user avatar
  • 21
2 votes
Accepted

Correlation between multiple time series

There is not one easy solution to your problem, as it is not well-defined, but there are ways to narrow it down and come closer to a solution. Here are some points that can help you to do so. 1. ...
Broele's user avatar
  • 1,352
2 votes
Accepted

What is the benefit of the exponential function inside softmax?

Usually the softmax is applied to logits (you can consider them as unnormalized log-probabilities), which are the output of the neural net. The logits are unbounded, i.e. they lie in $(-\infty, \infty)...
Luca Anzalone's user avatar
2 votes
Accepted

Cluster/Similarity problem with two datasets of different cardinality

There are different possible approaches. Without closer look into your data it is hard to tell which one would be the best. In the following, I will list multiple approaches. Pivot Table This is the ...
Broele's user avatar
  • 1,352
2 votes
Accepted

Which Python lib to use for classify data without training any model?

I think what you are going to achieve is possible with recent advances in: zero-shot learning and few-shot learning can let you build your classifier with little or no training data.: However note ...
Mario's user avatar
  • 414
2 votes

What methods do top Kagglers employ for score gain?

Top Kagglers often employ a combination of traditional machine learning techniques and creative, out-of-the-box strategies to gain an edge in competitions. Here are some techniques: 1. Ensemble ...
lvvittor's user avatar
2 votes

What methods do top Kagglers employ for score gain?

There are many methods that can be employed to increase your score on a Kaggle competition. Here are a few examples: Advanced classification techniques: Using advanced classification techniques such ...
Multivac's user avatar
  • 2,949
2 votes
Accepted

How does oversampling or undersampling approch is going to help during the testing on real time data?

The key here is how you define "help" regarding the measurement of performance. Oversampling/undersampling may not help increasing accuracy. However, it may help increase other performance ...
noe's user avatar
  • 25.6k
2 votes

How to impute and aggregate data with ID variant variables for predictive modeling?

This sounds like you're having issues grappling with relational theory. You have focused on the ID column as though it identifies an observed example. But your narrative ("multiple services")...
J_H's user avatar
  • 802
2 votes

ROC curve for a perfect model, why is AUC 1.0?

An explanation I find more insightful is by noting the probabilistic interpretation of AUC. For any random pair of positive and negative instance, AUC is the probability that the model gives a higher ...
Passer By's user avatar
  • 121
2 votes

Why does data science see class imbalance as a problem for supervised learning when statistics does not?

Statisticians typically focus on probabilistic classification. In the way they build their models they are interested in predicting $P(Y|X) = P(X|Y)P(Y)/P(X)$. Now we find : The predicted ...
Ggjj11's user avatar
  • 216
1 vote

Been stuck on a DS problem. Just need to know whether this problem statement is solvable or not

What your boss is telling you to do seems bassackwards to me given the clear business problem you've laid out (40% goes to manual approval and only 2% of those are rejected). What I really mean here ...
noNameTed's user avatar
1 vote

Which is the best binary classification model? Train and Test Accuracy are similar

Unfortunately, there is not golden rule which model is the best. It always depends on the use case and what is done with the model. In general, the metrics allow to compare different aspects, but it ...
Broele's user avatar
  • 1,352
1 vote

Why apply min-max normalization to each individual mel spectrogram for a training set?

Local normalization is commonly done in time series classification (TSC) tasks - not just for audio classification. But it may not be appropriate for every TSC task. The purpose is to remove ...
Lynn's user avatar
  • 1,274
1 vote
Accepted

Why my sentiment analysis model is overfitting?

There might be multiple reasons that might be the reason for overfitting some of which are: 1.) Scaling the data 2.) You have not mentioned which parameter values you have selected in the Tfidf ...
spectre's user avatar
  • 2,020
1 vote
Accepted

What is the minimum ratio to consider the data set as balanced for the classification algorithm?

Here's a table for 'degree of imbalance' for a binary classification problem. If you have multiple classes in your data set, I'd assume that if any of the classes deviates with a similar degree as ...
Stefan Popov's user avatar

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