Questions tagged [hyperparameter-tuning]

Hyperparameter tuning (also called hyperparameter optimization) refers to the process of finding the optimal set of hyperparameters for a given machine learning algorithm.

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Reduce Training steps for SSD-300

I am new to deep learning and I am trying to train my SSD-300 (single shot detector) model which is taking too long. For example even though I ran 50 epochs, it is training for 108370+ global steps. I ...
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BayesianOptimization tuning the same parameter with different results

I'm running a hyperparameter search using Keras wherein there is only one hyperparameter explicitly specified (# of LSTM units). However when running BayesianOptimization, after a while I notice it ...
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Tune learning rate while tuning other HP

When doing hyperparameters optimisation, like a Random Search, should you add a search space for the learning rate ? My intuition is that some HP might work better with a certain LR, and be sub-...
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Understanding 'C' value in Grid Search CV/ Parameter Tuning

This is the result of my hyperparameter tuning --> Best Penalty: l1 Best C: 59.94842503189409 How should I interpret the value of 'C'? I understand smaller the value of 'C', larger the ...
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Comparing accuracies of Grid Search CV & Randomized Search CV with K-Fold Cross Validation?

Are Grid Search CV & Randomized Search CV always/necessarily supposed to give more accurate results after hyperparameter tuning as compared to K-Fold Cross Validation?
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Python Script on tuning tree in orange datamining

Is there any python script that can be used to check the best tuning in tree model on orange python datamining? Since theres an error when I applied the script from http://docs.biolab.si/orange/2/...
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selection of loss function to avoid overfitting by autoencoder in prediction a figure with a sharp rise

I have to select the loss function to avoid overfitting by autoencoder in prediction of this figure that has a sharp raise, I would like to find how to avoid overfitting by autoencoder in prediction a ...
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21 views

Could I directly apply techniques for hyper-parameter tuning, and choose the best model?

I have noticed in some sources the author first trains the model (say a model from scikit-learn) with the default hyper-parameters, and the model naturally gives a ...
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Loss & accuracy curves from learning rate range test interpretation

I am working on a project doing experiments with the Learning Rate Range Test (See "A disciplined approach to neural network hyper-parameters: Part 1 -- learning rate, batch size, momentum, and ...
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How to improve regression neural network?

I am new to deep learning and data science and trying to increase my knowledge by working on some hackathons. Currently, the hackathon project I am working on has the task to predict the closing price ...
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22 views

Does hyperparameter tuning of Decision Tree then use it in Adaboost individually vs Simultaneously yield the same results?

So, my predicament here is as follows, I performed hyperparameter tuning on a standalone Decision Tree classifier, and I got the best results, now comes the turn of Standalone Adaboost, but here is ...
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52 views

Cross-validation split for modelling data with timeseries behavior

Background: I have a dataset that is generated every month (it is similar with card data that contains card demography and transactions every month and new accounts can be added in the middle of data ...
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lack of consistency in Bayesian optimization of xgboost's hyperparameters

I am trying to optimize the hyperparameters in an xgboost model using Bayesian optimization and the mlrmbo R package. The simplified code below seem to produce reasonable results, but the problem I ...
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Unable to tune hyperparameters for CatBoostRegressor

I am trying to fit a CatBoostRegressor to my model. When I perform K fold CV for the baseline model everything works fine. But when I use Optuna for hyperparameter tuning, it does something really ...
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GridSearch where input X consists of two DataFrames

For a project where a classifier and a regressor are combined in an scikit-learn pipeline, the input variable has to be a list (or sth equivalent) of two pandas DataFrames. When it comes to ...
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145 views

Flask output not showing

I am trying to deploy a XGBClassifier model using flask. After giving the values to the relevant fields on the webpage, the ...
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RandomizedSearchCV doesn't stop running

I'm trying to optimize the hyperparameters of my model using RandomizedSearchCV. However, it doesn't stop running even if I define few iterations. Someone could help me? The code I'm using is ...
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25 views

LightGBM boosting and bagging parameters

When training a gradient boosted decision tree model, I can use the LightGBM package to efficiently train my model. It's possible to define the hyperparameter search space with eg. ...
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Incremental Hyperparameter optimization in GPy classifier

Is there any way to do an epoch-wise incremental gradient descent hyperparameter optimization for the Gaussian Process class GPy.core.gp under the GPy package? I am familiar with the complete ...
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31 views

n_jobs=-1 or n_jobs=1?

I am confused regarding the n_jobs parameter used in some models and for CV. I know it is used for parallel computing, where it includes the number of processors specified in n_jobs parameter. So if I ...
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What would be a good n_estimators matrix and thus param_grid for this problem?

I am using GridSearchCV for optimising my predictions I am running a fairly large dataset and I am afraid I have not optimised the parameters enough. ...
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Rule of thumb for number of leafs and trees in a random forest?

I wonder if there is a rough estimate or rule of thumb or the like to guess the optimal number of leafs and trees in a random forest? Or would you just use a hyperparameter search method?
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64 views

Worse performance after Hyperparameter tuning

I first construct a base model (using default parameters) and obtain MAE. ...
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25 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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My own model trained on the full data is better than the best_estimator I get from GridSearchCV with refit=True?

I am using an XGBoost model to classify some data. I have cv splits (train, val) and a separate test set that I never use until the end. I have used GridSearchCV to determine the best parameters and ...
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How can a complex function be approximated using deep learning?

The insurance company I work for has a computationally intensive process to estimate future earnings based on tables of assumptions regarding price and probability of cancelation. I would like to ...
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43 views

Is Cross validation and GridSearchCV required every time we train a model?

I have a repetitive process that will build a model weekly based on the previous week's data. So while in development I tried GridSearchCV and cross-validation to ...
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Specifying parameter grid for regression model

I am working with more than one dataset. So, I have to test my Random Forest Model over 4 datasets. The parameter grid I am taking for dataset D1 is not producing good results for dataset D2 and so on....
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61 views

Hyper parameters (window size and vector dimensions) tuning in word2vec using Grey Wolf Optimization

Using Grey wolf Optimization, I want to calculate optimal values of two hyper parameters: context window size and embedding size (vector dimensions) for word2vec skipgram model used for word embedding....
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XGBoost regressor hyperparameter tuning with hyperopt leads to overfit

Using hyperopt to hyperparameter tuning on XGBoost regressor, I am receiving overfiting on the train set. There is any suggestion how to solve it ? I have used cross validation with ...
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31 views

Parameters calibration for a general DNN project

I am working on an RNN for a task of emotion recognition from speech. In order to set some parameters, I am following this post, and running a grid search over: The number of units in each RNN layer....
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Optimizing Decision Model with incomplete testset

I have a table of 2 features (numbers) on which I define a simple binary classification "model" (i.e. a simple logical expression) which needs 2 parameters thresholds. The model tries to ...
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XGBoost failing on highly imbalanced data!

I am working on a classification problem, where I am trying to predict a fraud login. The data is highly imbalanced i.e. 0 = non fraud logins , 1 = fraud logins 0 : 4538076 1 : 365 I have been trying ...
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Why does Adam optimizer work slower than Adagrad, Adadelta, and SGD for Neural Collaborative Filtering (NCF)?

I've been working on Neural Collaborative Filtering (NCF) recently to build a recommender system using Tensorflow Recommenders. Doing some hyperparameter tuning with different optimizers available in ...
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Tuning hyper parameters for different models with caretList

I'm trying to train an ensemble using the caretList function in the caret package. I'm using these models: ...
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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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Is it possible to fix the validation set when tuning hyperparameters using scikit learn?

I have a question regarding hyperparameter optimization in scikit learn. I am most familiar with tensorflow where you first split your data into three sets: Train, validation and test. Hyperparameters ...
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High Recall but too low Precision result in imbalanced data

I was training a model using XGBoost Classifier on a heavy imbalanced database with 232:1 of binary class. Because my training data contains 750k rows and 320 features (after doing many feature ...
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89 views

Using Sklearn's predefined split

I am working on a binary classification task using SVM. The dataset is quite large so I don't want to use k-fold CV for parameter tuning, but instead a simple train-validation-test split. I have done ...
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19 views

CNN Model Seems To Just Be Guessing

I am working with a binary classification problem, and regardless of what changes I make, the model seems to just be guessing between 0 (Negative) and 1 (Positive). The dataset is imbalanced at a ...
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53 views

Method of hyperparameter tuning for regression tree ensembles in Matlab

What regression tree ensemble methods and what parameters does Matlab actually consider in hyperparameter tuning? The documentation appears to be sparse, to say the least. See https://se.mathworks.com/...
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validation after hyperparameter tuning

I tuned my hyperparameter with random search and i used cv=5. Is it important to validated the hyperparameter with model and testdata or is it okay to use the given ...
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Results Random Forest Regression Optimization [duplicate]

I'm going to optimize the hyperparameters of a Random Forest Regressor using scikit-learn and GridSearchCV, with the following code: ...
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54 views

Hypertune xgboost to dealing with imbalanced dataset

My training data has extremely class imbalanced {0:872525,1:3335} with 100 features. I use xgboost to build classification model with bayessian optimisation to hypertune the model in range ...
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560 views

Classification Threshold Tuning with GridSearchCV

In Scikit-learn, GridSearchCV can be used to validate a model against a grid of parameters. A short example for grid-search cv against some of DecisionTreeClassifier parameters is given as follows: <...
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What is the best practice for tuning hyperparameters using validation data?

I'm building a binary classifier, using task-transfer from resnet and a total training set of 300 images. Initially I put aside 100 images as validation, and tuned the hyperparameters, each time ...
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92 views

GridSearch on imbalanced datasets

Im trying to use gridsearch to find the best parameter for my model. Knowing that I have to implement nearmiss undersampling method while doing cross validation, should I fit my gridsearch on my ...
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23 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 ...
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69 views

Getting very low/ wrong accuracy from RandomizedSearchCV

I am currently using RandomizedSearchCV to optimize my hyper-parameters. However the reported scores of each iteration is very low. When I then evaluate the highest scoring candidate I get very high ...

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