--- title: "Some inquisitive types of Machine Learning Algorithms used in Data Science" description: "There are many different types of Machine Learning algorithms that can be used to create models of complex datasets." author: "Drishan Vig" published: 2022-08-31 canonical: https://yourviews.mindstick.com/view/83713/some-inquisitive-types-of-machine-learning-algorithms-used-in-data-science category: "data science" tags: ["machine learning algorithms", "neural networks", "decision trees", "naive bayesian", "and boosting algorithms", "measurements", "naive bayes machine learning algorithms"] reading_time: 4 minutes --- # Some inquisitive types of Machine Learning Algorithms used in Data Science There are many different types of **Machine Learning algorithms** that can be used to create models of complex datasets. These algorithms include **Neural Networks, Decision trees, Naive Bayesian, and [Boosting algorithms](https://www.mindstick.com/forum/158516/what-is-the-difference-between-bagging-and-boosting-algorithms-provide-examples-of-each)**. Choosing the best algorithm for your project depends on your objectives and the data you need to analyze. ### Neural Networks - [**Neural networks**](https://en.wikipedia.org/wiki/Deep_learning) are **[artificial neural](https://www.mindstick.com/interview/34033/what-are-artificial-neural-networks-anns) networks** that process [large amounts of data](https://www.mindstick.com/forum/161107/how-does-mongodb-handle-large-amounts-of-data-and-maintain-performance) in parallel. They consist of several layers, and each tier receives inputs and outputs from the preceding tier. The first layer is **analogous to the optic nerve** of the human brain. The following layers receive signals from neurons further away from the optic nerve, and the last layer produces the output of the system. - The inputs to the network are combined and weighted based on their significance. These weights **determine the strength of interconnection** between the neurons. Each input is then added up and passed through an [activation function](https://answers.mindstick.com/qa/116389/what-is-an-activation-function). If the sum exceeds a threshold, the input to the next node is activated, and so on. This process makes the **neural network** a **feedforward network.** ### Decision trees - In **machine learning algorithms****,** one of the most popular techniques is to **use decision trees.** These algorithms are ideal for **classifying data.** The [process of building](https://www.mindstick.com/forum/158512/how-does-a-decision-tree-algorithm-work-describe-the-process-of-building-a-decision-tree) a **decision tree** involves **evaluating the values of several attributes**, including the binary target variable and the ph. Each attribute contributes a certain amount of information to the **classifier.** The more information the attribute provides to the classifier, the higher its importance in the tree. - **Decision trees** are also useful for **fault diagnosis.** For instance, a load-bearing rotatory machine requires measurements to determine if it is faulty. Because many **measurements** are needed to make a diagnosis, some may be irrelevant. In such cases, a **Decision Tree classifier** can quickly determine the **relevant measurements.** ### Naive Bayesian - **Naive Bayes** **machine learning algorithms** are based on the **principle of learning** how to recognize patterns from data. This **algorithm** works well with **categorical and numerical data**, but it's not always suitable for all types of data. For example, it's difficult to use **Naive Bayes** to detect the **presence of zeros in data**. Instead, you should use another technique to [solve this problem](https://answers.mindstick.com/qa/94726/my-phone-operates-automatically-how-to-solve-this-problem), such as **smoothing.** - A **Naive Bayes classifier** takes two tuples and labels them based on their attributes. Each tuple is represented by an **n-dimensional attribute vector.** Then, it computes the probability that the data representing one of those classes is true for each tuple. The **naive Bayes classifier** will then predict the class with the **highest posterior probability.** ### Boosting algorithms - **Boosting algorithms** use the outputs from a weak learner to create a stronger learner, and the aim is to improve the **prediction power of the model.** It works by paying higher attention to examples that are misclassified or exhibit higher error levels than the weak learner. In this article, we will look at the different types of **boosting algorithms, including AdaBoost, Gradient Boosting, and XGboost.** - **Boosting algorithms** are different from **decision trees,** but they work by combining weak learners to build a **strong learner.** The process involves **assigning weights to each observation** in the input data and then **re-weighting them** so that the final strong learner can perform better. The process repeats until the final output is accurate enough to be classified. ### K-nearest-neighbor algorithm - The **K-Nearest-Neighbor machine learning algorithm** for [data science](https://www.mindstick.com/services/data-science) is an effective method to classify datasets and make predictions. The algorithm has many advantages and can be applied to a variety of different tasks, including **classification, regression, and search**. However, the **K-NN algorithm** has a few limitations. First, it becomes increasingly slow as the number of examples and predictors increases. This makes it impractical to use **KNN in rapid prediction environments**. In such cases, a **faster algorithm** should be used. - Another **advantage of K-Nearest-Neighbor** is that it can help improve **classification performance.** The algorithm can **improve classification performance** by using **supervised metric learning**. It uses label information to help predict the **future value of an unknown variable.** --- Original Source: https://yourviews.mindstick.com/view/83713/some-inquisitive-types-of-machine-learning-algorithms-used-in-data-science Copyright © MindStick Software Pvt. Ltd. This Markdown version is provided for developers, AI systems, and offline reading.