When to use which machine learning algorithm? The right algorithm depends on the problem you’re solving. If you need to predict numbers, like house prices, linear regression is a good choice. For yes-or-no decisions, such as detecting spam, logistic regression works well. When the task ...
inline code can be used in machine learning algorithms to optimize performance in critical sections. however, it is important to ensure that the code does not introduce unintended biases or affect the integrity of the algorithm. how does inline code impact code documentation? inline code makes it...
Machine learning is taking the world by storm, and many companies that use rules engines for making business decisions are starting to leverage it. However, the two technologies are geared towards different problems. Rules engines are used to execute discrete logic that needs to have 100% precisio...
The “classic” application of logistic regression model is binary classification. However, we can also use “flavors” of logistic to tackle multi-class classification problems, e.g., using the One-vs-All or One-vs-One approaches, via the related softmax regression / multinomial logistic regres...
In the field of artificial intelligence, when we talk about machine learning algorithms, we often wonder how they can continuously improve their performance. One important factor is the data they use. So, which of the following statements about the role of data in machine learning algorithms is ...
Applications of Naive Bayes Algorithm As this algorithm is fast and efficient, youcan use it to make real-time predictions. This algorithm is popular for multi-class predictions. You can find the probability of multiple target classes easily by using this algorithm. ...
are adopting deep learning for various problems. Is the hype real or are linear models really all we need? The answer, as alwyas, is that it depends. In this post, I wanted to visit use cases in machine learning where deep learning would not really make sense to use as well as ...
Decision Trees are a supervised machine learning algorithm and are fairly intuitive to use. We make use of them every day to make decisions, even though we don’t refer to them as decision trees. For example, we may open our curtains in the morning and check what the weather is doing...
The main types of ensemble learning techniques or methods used for ensemble models are: Bagging Boosting Stacking Blending What is ensemble learning? Ensemble learning is a machine learning technique that describes the use of ensemble models, where multiple individual learning models are combined to imp...
as well as simulate weather, terrain, and so on. To a certain extent, it solves the problem of robots' "generalization ability". For example, in a simulation platform, different scenarios can be simulated to train robots to grab objects, and then the algorithm can be transferred to the rea...