Learning to Classify with Branching Tests: "A decision tree takes as input an object or situation described by a set of properties, and outputs a yes/no decision. Decision trees therefore represent Boolean functions. Functions with a larger range of outputs can also be represented...."
– Artificial Intelligence: A Modern Approach. By Stuart Russell & Peter Norvig. 2002. Section 18.3; page 531.
The models can have many hyperparameters and finding the best combination of the parameter using grid search methods. Grid search is a technique for tuning hyperparameter that may facilitate build a model and evaluate a model for every combination of algorithms parameters per grid. We might use 10 fold cross-validation to search the best value for that tuning hyperparameter. These values are called hyperparameters. To get the simplest set of hyperparameters we will use the Grid Search method.
Random Forest is a supervised machine learning algorithm that is composed of individual decision trees. This type of model is called an ensemble model because an "ensemble" of independent models is used to compute a result. The basis for the Random Forest is formed by many individual decision trees, the so-called Decision Trees. A tree consists of different decision levels and branches, which are used to classify data. The Decision Tree algorithm tries to divide the training data into different classes so that the objects within a class are as similar as possible and the objects of different classes are as different as possible. This tree helps to decide whether to do sports outside or not, depending on the weather variables "weather", "humidity" and "wind force".
AI is increasingly used in making decisions that impact us directly such as job applications, our credit rating, match-making on dating sites. So it is important that AI is non-discriminatory and that decisions do not favor certain races, gender, the color of skin. Discriminatory AI is a very wide subject going beyond purely technical aspects. However, to make it easily understandable, I will demonstrate how discriminatory AI looks using examples and visuals. This will give you a way to spot a discriminatory AI. Let me first establish the context of the example.
Neural networks are extremely convenient. They are usable for both regression and classification, work on structured and unstructured data, handle temporal data very well, and can usually reach high performances if they are given a sufficient amount of data. What is gained in convenience is, however, lost in interpretability and that can be a major setback when models are presented to a non-technical audience, such as clients or stakeholders. For instance, last year, the Data Science team I am part of wanted to convince a client to go from a decision tree model to a neural network, and for good reasons: we had access to a large amount of data and most of it was temporal. The client was on board, but wanted to keep an understanding of what the model based its decisions on, which means evaluating its features' importance.
Predicting the results of matches in sport is a challenging and interesting task. In this paper, we review a selection of studies from 1996 to 2019 that used machine learning for predicting match results in team sport. Considering both invasion sports and striking/fielding sports, we discuss commonly applied machine learning algorithms, as well as common approaches related to data and evaluation. Our study considers accuracies that have been achieved across different sports, and explores whether evidence exists to support the notion that outcomes of some sports may be inherently more difficult to predict. We also uncover common themes of future research directions and propose recommendations for future researchers. Although there remains a lack of benchmark datasets (apart from in soccer), and the differences between sports, datasets and features makes between-study comparisons difficult, as we discuss, it is possible to evaluate accuracy performance in other ways. Artificial Neural Networks were commonly applied in early studies, however, our findings suggest that a range of models should instead be compared. Selecting and engineering an appropriate feature set appears to be more important than having a large number of instances. For feature selection, we see potential for greater inter-disciplinary collaboration between sport performance analysis, a sub-discipline of sport science, and machine learning.
Last week I published two articles about Decision Trees: one about Decision and Classification Tree (CART) and another tutorial on how to implement Random Forest classifier. These two methods may look very similar, however there are important differences that every data professional or enthusiastic should know.
Originally published on Towards AI the World's Leading AI and Technology News and Media Company. If you are building an AI-related product or service, we invite you to consider becoming an AI sponsor. At Towards AI, we help scale AI and technology startups. Let us help you unleash your technology to the masses. The decision tree is one of the most powerful and important algorithms present in supervised machine learning.
The machine learning algorithms require a lot of mathematical operations and as the performance of the model improves, its mathematical operations also increase with complexity. A simple example of this can be the random forest and decision tree where the random forest is more accurate in maximum cases but has complex mathematics and takes more time than the decision trees. Robust modelling requires a process where large mathematical or numerical operations can be completed robustly. Jax is a library that can help us in improving the speed of mathematical operations. In this article, we will discuss the Jax library in detail.
Feature engineering plays a crucial role in many of the data modelling tasks. This is simply a process that defines important features of the data using which a model can enhance its performance. In time series modelling, feature engineering works in a different way because it is sequential data and it gets formed using the changes in any values according to the time. In this article, we are going to discuss feature engineering in time series and also we will cover an implementation of feature engineering in time series using a package called tsfresh. The major points to be discussed in the article are listed below.