Structured prediction energy networks (SPENs) (Belanger & McCallum, 2016) are a type of energy-based model (LeCun et al., 2006) in which inference is done by gradient descent.

The main idea is to demonstrate the effectiveness of these simple algorithms compared to the much more complex state-of-the-art RL algorithms proposed and evaluated on MuJoCo tasks. The results of the empirical evaluation are startling. The paper convincingly demonstrates very strong performance of the simple algorithm and policy class on the MuJoCo tasks. The evaluation is extremely thorough, the results are compelling and raise serious questions about the current state of RL algorithm evaluation methodology using MuJoCo. In my opinion, this paper is an excellent contribution to the RL literature.

Time-to-event analysis is a branch of statistics that has increased in popularity during the last decades due to its many application fields, such as predictive maintenance, customer churn prediction and population lifetime estimation. In this paper, we review and compare the performance of several prediction models for time-to-event analysis. These consist of semi-parametric and parametric statistical models, in addition to machine learning approaches. Our study is carried out on three datasets and evaluated in two different scores (the integrated Brier score and concordance index). Moreover, we show how ensemble methods, which surprisingly have not yet been much studied in time-to-event analysis, can improve the prediction accuracy and enhance the robustness of the prediction performance. We conclude the analysis with a simulation experiment in which we evaluate the factors influencing the performance ranking of the methods using both scores. Keywords: Ensemble methods, time-to-event analysis, integrated Brier score, concordance index.

This work explores the use of gradient boosting in the context of classification. Four popular implementations, including original GBM algorithm and selected state-of-the-art gradient boosting frameworks (i.e. XGBoost, LightGBM and CatBoost), have been thoroughly compared on several publicly available real-world datasets of sufficient diversity. In the study, special emphasis was placed on hyperparameter optimization, specifically comparing two tuning strategies, i.e. randomized search and Bayesian optimization using the Tree-stuctured Parzen Estimator. The performance of considered methods was investigated in terms of common classification accuracy metrics as well as runtime and tuning time. Additionally, obtained results have been validated using appropriate statistical testing. An attempt was made to indicate a gradient boosting variant showing the right balance between effectiveness, reliability and ease of use.

Hyperparameter tuning, any machine learning model training activity needs to be optimised. The learning process cannot extract the hyperparameters of a model from the provided datasets. However, they are extremely important for managing the actual learning process. These hyperparameters are derived from how machine learning models are mathematically formulated. For instance, while the learning rate in gradient descent is a parameter, the weights learned during the training of a linear regression model are parameters.

This article was published as a part of the Data Science Blogathon. Hyperparameter tuning or optimization is important in any machine learning model training activity. The hyperparameters of a model cannot be determined from the given datasets through the learning process. However, they are very crucial to control the learning process itself. These hyperparameters originate from the mathematical formulation of machine learning models. For example, the weights learned while training a linear regression model are parameters, but the learning rate in gradient descent is a hyperparameter.

Perform statistical analysis on real world datasets Understand feature engineering strategies and tools Perform one hot encoding and normalization Understand the difference between normalization and standardization Deal with missing data using pandas Change pandas DataFrame datatypes Define a function and apply it to a Pandas DataFrame column Perform Pandas operations and filtering Calculate and display correlation matrix heatmap Perform data visualization using Seaborn and Matplotlib libraries Plot single line plot, pie charts and multiple subplots using matplotlib Plot pairplot, countplot, and correlation heatmaps using Seaborn Plot distribution plot (distplot), Histograms and scatterplots Understand machine learning regression fundamentals Learn how to optimize model parameters using least sum of squares Split the data into training and testing using SK Learn Library Perform data visualization and basic exploratory data analysis Build, train and test our first regression model in Scikit-Learn Assess trained machine learning regression model performance Understand the theory and intuition behind boosting Train an XG-boost algorithm in Scikit-Learn to solve regression type problems Train several machine learning models classifier models such as Logistic Regression, Support Vector Machine, K-Nearest Neighbors, and Random Forest Classifier Assess trained model performance using various KPIs such as accuracy, precision, recall, F1-score, AUC and ROC. Compare the performance of the classification model using various KPIs. Apply autogluon to solve regression and classification type problems Use AutoGluon library to perform prototyping of AI/ML models using few lines of code Plot various models' performance on model leaderboard Optimize regression and classification models hyperparameters using SK-Learn Learn the difference between various hyperparameters optimization strategies such as grid search, randomized search, and Bayesian optimization. Assess trained model performance using various KPIs such as accuracy, precision, recall, F1-score, AUC and ROC. Compare the performance of the classification model using various KPIs.

In machine learning, hyperparameter optimization or tuning is the problem of choosing a set of optimal hyperparameters for a learning algorithm. A hyperparameter is a parameter whose value is used to control the learning process. By contrast, the values of other parameters are learned. In other words, hyperparameters are points of choice or configuration that allow a machine learning model to be customised for a specific task or dataset. Randomized Search is a method in which random combinations of hyperparameters are selected and used to train a model.

Model optimization is one of the toughest challenges in the implementation of machine learning solutions. Entire branches of machine learning and deep learning theory have been dedicated to the optimization of models. Hyperparameter optimization in machine learning intends to find the hyperparameters of a given machine learning algorithm that deliver the best performance as measured on a validation set. Hyperparameters, in contrast to model parameters, are set by the machine learning engineer before training. The number of trees in a random forest is a hyperparameter while the weights in a neural network are model parameters learned during training.