The classification algorithm is one of the most important aspects of supervised learning.
In this article, we will discuss various classification algorithms, such as logistic regression, Naive Bayes, decision trees, random forests, and so on. We will introduce each classification attribute of the algorithm of machine learning and how it works.
Logistic Regression Algorithm
Logistic regression can be a supervised learning classification algorithm that does not predict the probability of the target variable. It is one of the only ML algorithms that can be used for various classification problems (such as spam detection, diabetes prediction, cancer detection, etc.).
Logistic regression is easier to implement, explain, and guide truly effectively. If the number of observations is less than the number of characteristics, logistic regression should not be used; otherwise, over-adjustment will occur.
We use logistic regression to classify the data points binary. We classify and classify so that the output belongs to either of two classes (1 or 0). The two important parts of logistic regression are the hypothesis and the sigmoid curve. With the help of this hypothesis, we can derive the probability of the event.
The data generated according to this hypothesis can fit the logarithmic function that creates an S-shaped curve, which is called “sigmoid”. Using this log function, we can further predict the category of the class.
Machine learning classification algorithm
Classification is one of the most important aspects of supervised learning.
In this article, we will discuss various classification algorithms, such as logistic regression, Naive Bayes, decision trees, random forests, and so on. We will introduce each classification attribute of the algorithm and how it works.
The Naive Bayes algorithm is suitable for:
- Naive Bayes is a simple and fast method for predicting the category of a data set. Using it, multiple types of predictions can be made.
- When the independence assumption is valid, Naive Bayes is more capable than other algorithms (such as logistic regression).
However, Naive Bayes has the following disadvantages:
- If a categorical variable belongs to a category that does not follow in the training set, then the model will give it a probability of 0, which will prevent it from making predictions.
- Naive Bayes assumes that its characteristics are independent. In real life, it is difficult to collect data that involves completely independent characteristics.
Decision tree algorithm
A decision tree algorithm uses itself for prediction and classification in machine learning.
Using a decision tree with a given input set, you can map various results as consequences or decision results.
Suppose you only buy when the shampoo runs out. If you do not have shampoo, you will evaluate the weather outside to see if it is raining. If it does not rain, it will disappear, if it does not rain, it will disappear.
This decision tree is the result of several levels of steps that can help you make certain decisions. To build this tree, there are two steps: induction and pruning. In induction, we build a tree, and in pruning, we remove all the complexity of the tree.
The most famous neighbor algorithm
The most famous neighbor is one of the most basic but important classification algorithms in machine learning.
KNN belongs to the field of supervised learning and has many applications in pattern recognition, data mining, and intrusion detection. These KNNs are used in real-life scenarios that require non-parametric algorithms. These algorithms make no assumptions about how the data is distributed.
When we obtain previous data, KNN classifies the coordinates identified by specific attributes.
Support Vector Machine Algorithm
Support Vector Machine is a supervised machine learning algorithm that provides data analysis for classification and regression analysis.
Although they can be used for regression, SVM is used primarily for classification. We draw in n-dimensional space. The value of each character is also the value of the specified coordinate. Next, we find the ideal hyperplane that distinguishes these two classes.
These support vectors are the coordinate representations of individual observations. It is a boundary method that separates two classes.
Random Forest Algorithm
The Random Forest Classifier is an ensemble learning method for classification, regression, and other tasks that can be performed with the help of decision trees. These decision trees build themselves during training and the class result sort and roll back themselves.
With the help of these random forests, the habit of over-tightening the training set to correct itself.
Random Forest Classifiers are as follows:
Pros-Random Forest Classifiers help reduce model overfitting, and in some cases, these classifiers are more accurate than decision trees.
Disadvantages: Random forests exhibit real-time predictions, but are inherently slow. They are also difficult to implement and have complex algorithms.
Stochastic gradient descent algorithm
Stochastic gradient descent (SGD) is a class of machine learning algorithms suitable for large-scale learning. This is an efficient method for discriminative learning of linear classifiers under the convex loss function of linear regression (SVM) and logistics.
We apply SGD to large-scale machine learning problems in text classification and other fields of natural language processing. It can effectively extend itself to problems with more than 10 ^ 5 training examples and by providing more than 10 ^ 5 functions.
The following are the advantages of stochastic gradient descent:
- These algorithms are effective. We can easily implement these algorithms.
- However, Stochastic Gradient Descent (SGD) has the following disadvantages: The SGD algorithm requires many hyperparameters, such as regularization and multiple iterations.
- SGD algorithm is also very sensitive to feature scaling, which is one of the most important steps in data preprocessing.
Kernel approximation algorithm
In this sub-module, several functions can approximate feature maps, which correspond to certain kernels used as examples in support vector machines. These feature functions perform a wide range of nonlinear input transformations as the basis for linear classification or other algorithms.
Compared with kernel techniques, one advantage of using fuzzy features, which are also explicit in nature, is that explicit assignment is better in online learning, which can significantly reduce the cost of learning on very large data sets.
Machine learning is a branch of artificial intelligence (AI) and computer science that focuses on using data and algorithms to imitate human learning methods and gradually improve its accuracy.
In general, we understand the various algorithms used for machine learning classification. These algorithms use themselves for various classification tasks. We also analyze its advantages and limitations.
For more details, CLICK HERE.