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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22 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 (...
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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?
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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 ...
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31 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: ...
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34 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, ...
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Can I dynamically change the hyper-parameters of a model?

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 ...
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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. ...
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35 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 ...
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53 views

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 ...
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554 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 ...
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18 views

How to tune the hyperparameters in graphical models such as a Markov random field?

What's a good way to tune the hyperparemter of a Markov random field (MRF) with a Gaussian prior that's not uncommon in image segmentation tasks? The structure of the graphical model is not a big ...
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160 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 ...
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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 ...
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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 ...
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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 <...
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97 views

Good mathematically explained algorithm for Hyperparameter Optimization (Bayesian) for implementing in Java

I have implemented Random Forest, Bagging, Gradient Boosting, etc... in java myself. It took a long time to complete these machine learning algorithm codes. But at last all of them are well running. ...
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81 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-...
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145 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 ...
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432 views

XGboost and regularization

Does the XGBClassifier method utilizes the two regularization terms reg_alpha and reg_lambda,...
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38 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 ...
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120 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 ...
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3answers
11k 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 ...
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1k 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 ...
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34 views

Two questions on hyper-parameter tuning

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 ...
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42 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 ...
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206 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'...
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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), ...
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25 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 ...
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581 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 ...
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19 views

Techniques for hyperparameter search in non-stationary environments

I'm tuning a supervised machine learning model over time, also called incremental learning. I do not want to assume the environment is non-stationary. Grid search and random search do not appear to ...
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1answer
1k views

Small number of estimators in gradient boosting

I am tuning a regression gradient boosting-based model to determine the appropriate hyperparameters using 4-folds cross validation. More specifically, I am using XGBoost and lightGBM for the models ...
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386 views

ROC AUC score is much less than average cross validation score

Using Lending club Dataset to find the propability of default. I am using hyperopt library to fine tune hyper parameter for an XGBclassifier and trying to maximize the ROC AUC score. I am also using ...
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1answer
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Do i need to use hyperparamters from Gridsearch to train on WHOLE training set to get final model?

I just want to make sure i am on the right lines so please correct me if wrong. I am testing which hyperparmets are best for logisitic regession on my data X, y where X is featrues and y is target. X, ...
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2k views

How can I tune LSTM hyperparameters?

If anyone is there to answer these, that'll be great. I'm in the midst of a Final Year Project on LSTM. Currently, I’m stuck and confused over LSTM codes. There are 4 hyperparameters that I can play ...
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70 views

Search for hyperparameters whith different features using Random Forest

I have a dataset in which I would like to perform a classification model, so I have decided to use Random Forest. The number of features that I have is approximately 200 and I would like to test which ...
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155 views

Validation curve/RandomizedSearchCV difference train and test score

Ive build a RF model for an imbalanced data set that after feature selection has an F1 score of 54.26%. I am now trying to do hyper parameter tuning using RandomizedSearchCV, after creating validation ...
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30 views

Parameter optimization and selection in dynamic neural networks

I have used a Bayesian optimization to tune machine learning parameters. The optimized parameters are "Hidden layer size" and "...
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1answer
302 views

Hyperparameter optimization performance comparison

I have used Bayesian optimization for hyperparameter tuning in a machine learning model. What is the best way to compare the performance of network with and without Bayesian optimization? I found some ...
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70 views

SVM C vs gamma hyperparameter tuning

While running SVC(), how we can hyperparameter tune C vs gamma combination? I could see changes in C and gamma are impacting the accuracy differently. Also, what I understand about C and gamma are: C ...
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133 views

Why do BERT classification do worse with longer sequence length?

I've been experimenting using transformer networks like BERT for some simple classification tasks. My tasks are binary assignment, the datasets are relatively balanced, and the corpus are abstracts ...
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2answers
1k views

Why SVM gridsearch takes longer time?

I have a dataset of 5K records and 60 features focussed on binary classification. Please find my code below for SVM paramter tuning. It's running for a longer time than ...
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47 views

Is copying parameter considered as plagarism?

So my friends and i are writing a kaggle assignment and the base code is written by me. One of my friend use my base code(feature engineering, labeling, etc.) and put it into a loop to find the best ...
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100 views

Hyperparameter optimization, ensembling instead of selecting with CV criteria

While burning CPUs performing a CV selection on a thin grid put on some hyperparameter space. I am using the `scikit-learn' API, for which the end result is a single point on the hyperparameter space, ...
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1answer
81 views

ML in R (caret-package) missing hyperparameters

I have a pretty specific question regarding the caret package however I still hope to finde help here. I recently worked with the caret package and trained a multilayer perceptron with ...
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152 views

is learning rate schedule a hyperparameter? [duplicate]

I believe term ‘learning rate schedule’ is a certain solution for tuning the learning rate. But at the same time, every parameter evaluating the parameter itself can be called a hyperparameter. So can ...
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1k views

Grid search or gradient descent?

Assume we have a neural network and one if its activation functions is a function of parameter a. We want to find the weights and parameter a that leads to the minimum loss on the validation set which ...
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59 views

Difference between validation and prediction

As a follow-up to Validate via predict() or via fit()? I wonder about the difference between validation and prediction. To keep it simple, I will refer to train, <...
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455 views

Hyperparameter tuning and cross validation

I have some confusion about proper usage of cross-validation to tune hyperparameters and evaluate estimator performance and generalizeability. As I understand it, this would be the process you would ...
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1answer
59 views

Similarity of XGBoost models?

Is xgboost with n_estimators = 100 and learning_rate = 0.1, same as xgboost with n_estimators = 50 and learning_rate = 0.2 ?