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Questions tagged [hyperparameter]

Hyperparameters of a model are the kind of parameters that cannot be directly learned during training but are set beforehand. Hyperparameters can define, for example, the complexity of the model or its capacity to learn.

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Optimal combination of variables to minimise output

To be honest I'm not 100% sure how much this is purely a coding issue or a data science issue, but I'll take my chances. I've developed a matrix which is a mixture of various hyperparameters, the ...
Dante Saint-Germain's user avatar
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Hyperparameter Widget

Could you please inform me if there exists a widget designed for the purpose of conducting hyperparameter optimization? I attempted to locate such a tool, but regrettably, I was unable to find one.
Gerardo's user avatar
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Will hypermeters tuned on sampled dataset work for the whole dataset?

I'm doing multi-label classification on text data using BERT model. Since the dataset is huge, around 50 thousand rows, I was thinking to use stratify sampling on dataset to reduce it to around 2-4 ...
Shaurya Uniyal's user avatar
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How does deeper or shallower trees (higher or lower max_depth) affect xgboost model?

I am doing an xgboost model for landslides assessment and I am using max_depth as one of my hyperparameters, but I don't understand how does it affect model ...
Omab's user avatar
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Hyperparameter tuning

Jane trains three different classifiers: Logistic Regression, Decision Tree, and Support Vector Machines on the training set. Each classifier has one hyper-parameter (regularisation parameter, depth-...
Tom's user avatar
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1 answer
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Ordering of Train/Val/Test set use in hyperparameter tuning

The way I read almost lots of ML advice on these datasets sounds like "You train a model that's randomly chosen hyperparameters first on the training set, then you ignore this bit of the work, ...
Socorro's user avatar
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1 answer
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Optimizing decision tree

I have a question regarding the technique/technology which could be applied for the issue: Suppose I have a rule-based tree or decision tree which predicts a variable Y based on variables A,B,C. This ...
DannyV's user avatar
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why is there no research on machine learning algorithms to determine optimal hyperparameters for metaheuristics?

I am not shure if I am in the correct forum for this question. I'm sorry, if I'm in the wrong place here. Question: Why is there no research on machine learning algorithms to determine hyperparameters ...
Andre's user avatar
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Can somebody explain to me, how the Hyperparameters in this expamle work? I only uderstood the meaning for gamma

I am quite new to this topic, but I want to understand how Reinforcement Learning (RL) works in this example (https://gymnasium.farama.org/tutorials/training_agents/reinforce_invpend_gym_v26/). I have ...
Peter's user avatar
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1 answer
70 views

Visualizing correlation between hyper-parameters and metrics for Neural Network

I am working with a neural network and I want to investigate how different settings affect the loss and standard deviation of the network. I can change various parameters such as the loss function, ...
Chris Ze Third's user avatar
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1 answer
393 views

Is hyperparameter tuning done on training or validation data set?

Is hyperparameter tuning done on training or validation data set? The post here gives mixed opinion as of whether the training set should be used for hyperparameter tuning. And I would like to know ...
Student's user avatar
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Do transformers (e.g. BERT) have an unlimited input size?

There are various sources on the internet that claim that BERT has a fixed input size of 512 tokens (e.g. this, this, this, this ...). This magical number also appears in the BERT paper (Devlin et al. ...
Mew's user avatar
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1 vote
1 answer
170 views

Is hyperparameter tuning with different samples of data on each run a bad idea?

I have 2k time series and want to optimize the hyperparameters of my prophet model. It takes 1 hour to train and evaluate on every time series for each hyperparam combination. So, I want to run it on ...
codeananda's user avatar
1 vote
0 answers
119 views

Why is a neural network not doing better than multivariate linear regressions?

I am making neural networks of multiple targets, all using same training data. For some of these targets, multivariate linear regressions do a very good job, i.e. a strong linear relation exists ...
Socorro's user avatar
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Relation between batch size, number of steps, and learning rate

Taking alphazero training setup as a reference: 700k total steps batch-size of 4096 initial LR of 0.2 What would be an equivalent setup for a batch-size of 1024? ...
danny's user avatar
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1 answer
187 views

Different results between hyperparameter optimisation and actual training/val values

If I want to do a hyperparameter optimisation on a dataset using e.g. hyperband or random search, I note that some of the models being randomly chosen seem to have rather good R2 scores, MSE etc. I ...
Socorro's user avatar
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Feature Selection - Comparing Performance of different size datasets

If I have training data X, with N features, and I do feature selection, and discover n of <...
Socorro's user avatar
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3 answers
2k views

Why does hyperparameter tuning occur on validation dataset and not at the very beginning?

Despite doing/using it a few times, I'm still slightly confused by the use of a validation set for hyper parameter tuning. As far as I can tell, I choose a model, train it on training data, assess ...
Socorro's user avatar
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1 answer
5k views

How to suppress "Estimator fit failed. The score on this train-test" warning message?

I am working on hyper-tuning random forest classifier with following parameters in random search CV ...
data science student's user avatar
3 votes
1 answer
1k views

Estimating Length of Hyperband Trials in Advance

I would like to use the (Keras/Tensorflow) hyperband tuning algorithm more than the Keras random search, for instance, when testing hyperparameters. With random search I can set max trials and get a ...
Socorro's user avatar
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1 answer
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Training Loss or Validation Loss for Hyperparameter Optimisation

When performing HO, should I be looking to train each model (each with different hyperparameter values, e.g. with RandomSearch picking those values) on the training data, and then the best one is ...
Socorro's user avatar
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Scikit Learn Random Forest Classifier Hyperparameter Min Target Sample Size

From reading the docs on Scikit Learn, I haven't been able to find an answer, but does anyone know if there is a way to specify to always include a specific number out of the max sample size of ...
Bipbupbop's user avatar
1 vote
1 answer
11k views

Optimal batch size and number of epoch for BERT

I use this tutorial https://www.tensorflow.org/text/tutorials/classify_text_with_bert and get different accuracy depend on epoch numbers and batch sizes. What's optimal parameters?
Dmitry  Sokolov's user avatar
1 vote
1 answer
53 views

Hyperparameter searching when there is no development set

I have a train and a test set and no development (dev) set. I'm training a model on the train set and searching for the best hyperparameters that can eventually maximize the accuracy of the test set (...
Pedram's user avatar
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Can we make an AI to fine tune other AI hyper parameters?

Every time AI gurus talk about fine tuning hyper parameters, they more or less say it's trial and error. But can't we make an AI to tell AI what its hyper parameters should be?
Shantanu's user avatar
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Help with Classification using scikit-learn models [closed]

I'm using the Titanic data set to classify the missing Cabins. There is a lot of missing Cabin values. My objective is just to assign the letter of the Cabin without the room number. So, I'm just ...
idkfa.bfg2's user avatar
1 vote
1 answer
345 views

What does updated alpha mean in LDA model?

I'm trying to understand LDA model by reading through implementations of the algorithm. Many implementations update alpha during training iterations with codes like: ...
user115919's user avatar
1 vote
2 answers
606 views

select hyperparameters using Latin hypercube sampling (LHS) from a large matrix/grid of parameter combinations

I have a matrix with each row corresponds to a hyperparameter for the XGBoost model. There are seven parameters to tune in XGBoost (as shown below: nrounds/iterations, max_depth, eta, gamma, ...
user32145's user avatar
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1 vote
1 answer
88 views

Can I apply different hyper-parameters for different sliding time windows?

Question Can I apply different hyper-parameters for different training sets? I can see the point of using the shared parameters but I cannot see the point of using shared hyper-parameters. The ...
Eiffelbear's user avatar
1 vote
0 answers
65 views

How to compare hyperparameter tuning in R and Python

I tried random forest in both R (Caret) and Python (Scikit-learn), but the results differ drastically. Pearson correlation between predicted value and actual value was 0.2 in python whereas 0.8 in R. ...
user110735's user avatar
1 vote
3 answers
90 views

Using Transaction Amount to Guide Learning in an Fraud Detection Machine Learning Model

I am currently using transaction amount as a feature in an XGBoost classification model designed to identify fraudulent transactions. Furthermore, transaction amount is bounded for this problem ...
Charles's user avatar
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1 vote
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XGBoost: Typical gamma and min_child_weight range

What is the typical accepted range of gamma and min_child_weight parameters for the XGBoost algorithm? Is the range of min_child_weight correlated with the number of feature or samples in the training ...
thereandhere1's user avatar
0 votes
1 answer
3k views

BERT minimal batch size

Is there a minimum batch size for training/re-fining a BERT model on custom data? Could you name any cases where a mini batch size between 1-8 would make sense? Would a batch size of 1 make sense at ...
Predicted Life's user avatar
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0 answers
560 views

Hyper tuning reduce the accuracy score, why?

I have performed hyper tuning grid CV search on KNN model. The actual accuracy score for my KNN was accuracy of 42.31 % without performing hyper tuning. However, after performing hyper tuning, the ...
Mara Bella's user avatar
0 votes
1 answer
28 views

How to distinguish between different values of a hyperparameter in communication?

From Wikipedia: In machine learning, a hyperparameter is a parameter whose value is used to control the learning process. If we go by the definition of parameter in What's the difference between an ...
Mario Ishac's user avatar
2 votes
2 answers
419 views

Faster grid search with small dataset to derive best params instead of full dataset?

I have a dataset of 300 000 rows and an ensemble model, which include grid search to find the best params of every algorithm. Unfortunately the grid search needs to long and I have problems to ...
martin's user avatar
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1 vote
0 answers
259 views

Adaptive Resampling in Caret with Pre-specified Validation Set

I was wondering if this is the correct way to get adaptive sampling in caret working with a pre-specified validation set using index. I can get this to work using the 'cv' method in caret like so <...
jtanman's user avatar
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0 votes
1 answer
364 views

DBSCAN Clustering

I used K-means to get the number of clusters for my data(Elbow Method). Then I was trying to see if for some specific hyperparameters can we get the same number of clusters for DBSCAN. I tried Brute-...
Tushar Pandey's user avatar
0 votes
1 answer
826 views

Final Model fitting - subset vs entire training data

If I used a subset of the entire available training data for model tuning and hyperparamater selection, should I fit the final model to the subset training dataset or the entire available training ...
thereandhere1's user avatar
1 vote
1 answer
2k views

XGboost and regularization

Does the XGBClassifier method utilizes the two regularization terms reg_alpha and reg_lambda,...
thereandhere1's user avatar
1 vote
1 answer
92 views

Tuning SVM C parameter

I would like to ask for help regarding my model. I have a dataset of preprocessed images and I performed a binary classification with SVM on Python. I tuned the value of the c parameter from 0.001 to ...
Farid Ben Ali's user avatar
0 votes
1 answer
811 views

Hill Climbing Algorithm - Optimum Step Size

I am implementing a standard hill climbing algorithm to optimise hyper-parameters for a predictive model. The hill climbing algorithm is being applied as part of a two-stage approach: Apply grid ...
A_Murphy's user avatar
2 votes
3 answers
22k views

Hyper-parameter tuning of NaiveBayes Classier

I'm fairly new to machine learning and I'm aware of the concept of hyper-parameters tuning of classifiers, and I've come across a couple of examples of this technique. However, I'm trying to use ...
Sameer Zahid's user avatar
9 votes
2 answers
4k views

XGBoost and Random Forest: ntrees vs. number of boosting rounds vs. n_estimators

So I understand the main difference between Random Forests and GB Methods. Random Forests grow parallel trees and GB Methods grow one tree for each iteration. However, I am confused on the vocab used ...
Jack Armstrong's user avatar
1 vote
1 answer
74 views

Two questions on hyper-parameter tuning [closed]

Question 1: In the example of logistic regression, I often see the regularization constant and penalty methods being tuned by a grid search. However, it seems like there are a lot more options for ...
rocksNwaves's user avatar
0 votes
1 answer
74 views

Hyperparameter tuning of neural networks using Bayesian Optimization

One of the assumptions for finding good hyperparameters using Bayesian optimization (GP) is that the unknown function is smooth. Is this assumption valid for neural networks or at least for most of ...
Angadishop's user avatar
1 vote
2 answers
659 views

Is the search space of Hyperparameters Continuous or Discrete?

I am looking into hyper-parameter tunning and was curious about whether the search space is considered continuous or discrete? My understanding of both those cases: 1. Continuous would make it 'easier'...
loulours's user avatar
1 vote
1 answer
39 views

Asynchronous Hyperparameter Optimization - Dependency between iterations

When using Asynchronous Hyperparameter Optimization packages such as scikit optimize or hyperopt with cross validation (e.g., cv = 2 or 4) and setting the number of iteration to N (e.g., N=100), ...
thereandhere1's user avatar
1 vote
0 answers
46 views

Classification Model showing different accuracy for SAME data?

This is my first post here, so kindly pardon any commonplace errors. So, i have been training an XGBoost multi-class model on Google Colab. I am using a balanced dataset, with 31000 rows, where each ...
Muhammad Yasir's user avatar
4 votes
2 answers
1k views

Shuffle the data before splitting into folds

I am running a 4-folds cross validation hyperparameter tuning using sklearn's 'cross_validate' and 'KFold' functions. Assuming that my training dataset is already shuffled, then should I for each ...
thereandhere1's user avatar