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How to tune hyperparameter for a logistic regression model?

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To tune hyperparameters for a logistic regression model, various optimization techniques can be employed. Bayesian optimization is a popular method that can effectively tune hyperparameters for classifiers like logistic regression . Another approach is sequential model-based optimization (SMBO), which constructs a common response surface for all datasets, allowing for efficient hyperparameter tuning across different datasets . Additionally, a novel calibration scheme for penalized logistic regression has been introduced, offering optimal finite sample bounds and easy implementation for tuning parameters effectively . These methods provide efficient ways to optimize hyperparameters for logistic regression models, enhancing their performance in classification tasks.

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Open access•Journal Article•DOI Fast hyperparameter tuning using Bayesian optimization with directional derivatives Tinu Theckel Joy, Santu Rana, Sunil Gupta, Svetha Venkatesh - Show less +3 more 12 Oct 2020-Knowledge Based Systems 27 Citations	Utilize Bayesian optimization with directional derivatives to tune hyperparameters for a logistic regression model efficiently, leveraging insights from statistical learning theory for classifiers.
Open access•Book Chapter•DOI Hyperparameter Tuning of the Model for Hunger State Classification Mohd Azraai Mohd Razman, Anwar P. P. Abdul Majeed, Rabiu Muazu Musa, Zahari Taha, Gian Antonio Susto, Yukinori Mukai - Show less +5 more 03 Jan 2020	Bayesian optimization was used to refine hyperparameters in the study. For logistic regression, this method can enhance model performance by adjusting parameters like regularization strength and solver choice.
Open access•Journal Article•DOI A hyper-parameter tuning approach for cost-sensitive support vector machine classifiers Rosita Guido, Maria Carmela Groccia, Domenico Conforti - Show less +2 more 02 Feb 2022-Soft Computing 4 Citations	Not addressed in the paper.
Open access•Posted Content Tuning Parameter Calibration in High-dimensional Logistic Regression With Theoretical Guarantees Wei Li, Johannes Lederer - Show less +1 more 01 Oct 2016-arXiv: Methodology 1 Citations	A novel calibration scheme for penalized logistic regression utilizes simple tests along the tuning parameter path, offering optimal finite sample guarantees and efficient implementations.
Open access•Proceedings Article Efficient Transfer Learning Method for Automatic Hyperparameter Tuning Dani Yogatama, Gideon S. Mann - Show less +1 more 02 Apr 2014 174 Citations	The paper proposes an efficient transfer learning method using sequential model-based optimization to tune hyperparameters for logistic regression models across datasets, showing superior performance.

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Does Support Vector Regression (SVR) model have hyperparameter tuning issues?5 answersSupport Vector Regression (SVR) models do have hyperparameter tuning issues. The selection of hyperparameters C and γ is crucial for optimizing the performance of SVR models. Various optimization algorithms have been proposed to solve this non-convex optimization problem, including grid search, random search, Bayesian optimization, simulated annealing, particle swarm optimization, and others. Additionally, there have been proposals to decouple the selection of C and γ. The challenge lies in finding the right balance between accuracy and generalizability of the SVR model predictions, which requires experimentation and tuning of the hyperparameters. However, the time cost and forecast accuracy of parameter adjustment can be challenging, especially for big data prediction. Despite these challenges, SVR models have shown promise in various applications, such as well-location optimization and reflectarray antenna design.

Does back propagation neural network require hyperparameter tuning ?5 answersBack propagation neural networks do require hyperparameter tuning. The large number of hyperparameters in deep learning models can lead to errors if they are set manually. Hyperparameter tuning methods such as grid search, random search, and Bayesian Optimization are commonly used to find the optimal values for these hyperparameters. Bayesian Optimization, in particular, builds a surrogate model of the objective function and uses an acquisition function to decide where to sample new hyperparameters. This helps in improving the performance of deep neural networks by finding the best combination of hyperparameters.

What is the best way to tune the hyperparameters of AdaBoost?3 answersThe best way to tune the hyperparameters of AdaBoost is to use a guided automated hyperparameter optimization approach. This approach helps in discovering the trend and gaining insights into the relationship between the performance of the machine learning models and their hyperparameters. It enables users to focus on the most important hyperparameters and choose adequate hyperparameter spaces for tuning. Additionally, using the Design of Experiments (DOE) methodology, such as factorial designs, for screening and Response Surface Methodology (RSM) for tuning can lead to better parameter selection and optimal results. Another efficient approach is to adjust the hyperparameters to optimize the approximate leave-one-out formula (ALO) using gradient and hessian computations and a second-order optimizer. Overall, a guided automated hyperparameter optimization approach and the use of statistical methodologies can help in effectively tuning the hyperparameters of AdaBoost.

What is the best way to tune hyperparameters?4 answersThe best way to tune hyperparameters is by adopting established best practices from AutoML, such as the separation of tuning and testing seeds, as well as principled hyperparameter optimization (HPO) across a broad search space. This approach has been shown to significantly affect the agent's final performance and sample efficiency in deep reinforcement learning (RL). Comparisons between multiple state-of-the-art HPO tools and hand-tuned counterparts have demonstrated that HPO approaches often have higher performance and lower compute overhead. Additionally, efficient formulas to compute the gradient and hessian of approximate leave-one-out cross-validation have been derived, allowing for the application of second-order optimizers to find hyperparameters. It is important to note that hyperparameter optimization is more nuanced than previously believed, and the specific optimizers that should be endorsed remain to be seen.

How to improve a logistic regression model?4 answers

How to make a logistic regression model better?6 answers

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