The simplest model is the Decision Tree. A combination of Decision Trees builds a Random Forest. Random Forest usually has higher accuracy than Decision Tree does. A group of Decision Trees built one after another by learning their predecessor is Adaptive Boosting and Gradient Boosting Machine. Adaptive and Gradient Boosting Machine can perform with better accuracy than Random Forest can. Extreme Gradient Boosting is created to compensate for the overfitting problem of Gradient Boosting. Thus, we can say that in general Extreme Gradient Boosting has the best accuracy amongst tree-based algorithms. Many say that Extreme Gradient Boosting wins many Machine Learning competitions. If you find this article useful, please feel free to share.
Most Professional Machine Learning practitioners follow the ML Pipeline as a standard, to keep their work efficient and to keep the flow of work. A pipeline is created to allow data flow from its raw format to some useful information. All sub-fields in this pipeline's modules are equally important for us to produce quality results, and one of them is Hyper-Parameter Tuning. Most of us know the best way to proceed with Hyper-Parameter Tuning is to use the GridSearchCV or RandomSearchCV from the sklearn module. But apart from these algorithms, there are many other Advanced methods for Hyper-Parameter Tuning.
From the past few years, due to advancements in technologies, the sedentary living style in urban areas is at its peak. This results in individuals getting a victim of obesity at an early age. There are various health impacts of obesity like Diabetes, Heart disease, Blood pressure problems, and many more. Machine learning from the past few years is showing its implications in all expertise like forecasting, healthcare, medical imaging, sentiment analysis, etc. In this work, we aim to provide a framework that uses machine learning algorithms namely, Random Forest, Decision Tree, XGBoost, Extra Trees, and KNN to train models that would help predict obesity levels (Classification), Bodyweight, and fat percentage levels (Regression) using various parameters. We also applied and compared various hyperparameter optimization (HPO) algorithms such as Genetic algorithm, Random Search, Grid Search, Optuna to further improve the accuracy of the models. The website framework contains various other features like making customizable Diet plans, workout plans, and a dashboard to track the progress. The framework is built using the Python Flask. Furthermore, a weighing scale using the Internet of Things (IoT) is also integrated into the framework to track calories and macronutrients from food intake.
How to use classification techniques to support automated manufacturing process design from product specifications. As we saw in our article series on AI in Manufacturing, manufacturing plants offer one of the most complex yet most promising environments to deploy large-scale AI-based solutions. In this article we provide a very concrete use-case where a machine learning algorithm is used to learn from historical production data how to infer the number of necessary manufacturing steps solely based on the basic product specifications. A manufacturer specializes in the custom-production of small mechanical parts. Their customers provide them with specifications of the parts to be produced. The manufacturer must first determine the exact steps to follow to produce these parts and then decide on appropriate price quotations.