Driven by the success of neural networks, more and more researchers and developers have begun to re-examine machine learning, and began to try to use some machine learning methods to automatically solve the problem of easy data collection. However, among the many machine learning algorithms, which ones are quick and powerful, suitable for novices to learn?
There is a "no free lunch" theorem in machine learning. In a simple explanation, it means that no algorithm can be applied to all problems, especially in supervised learning.
For example, you can't say that a neural network must be better than a decision tree, and vice versa. To judge the pros and cons of the algorithm, the size and structure of the data set and many other factors are crucial. So, you should try different algorithms for your problem. Then use the retained test set to evaluate the performance and choose a better algorithm.
Of course, the algorithm must be suitable for your problem. For example, if you want to clean your house, you need a vacuum cleaner, a broom, and a mop. Instead of picking up the shovel to start digging the ground.
However, for predictive modeling, there is a general principle that applies to all supervised learning algorithms.
The machine learning algorithm can be described as learning an objective function f that best maps the input variable X to the output variable Y. There is a general class of learning tasks. We want to predict Y based on the input variable X. We don't know what the objective function f looks like. If we have known it for a long time, we can use it directly without having to learn from the data through machine learning algorithms.
The most common machine learning is to learn the mapping of Y=f(X) and predict Y for the new X. This is called predictive modeling or predictive analysis. Our goal is to make predictions more accurate.
For newcomers who want a basic understanding of machine learning, here are 10 machine learning algorithms that data scientists use most often.
Linear regression
Linear regression is probably one of the most well-known algorithms in the field of statistics and machine learning.
At the expense of interpretability, the primary goal of predictive modeling is to reduce model errors or optimize prediction accuracy. We have borrowed a variety of algorithms from different fields such as statistics to achieve this goal.
Linear regression finds a set of specific weights called coefficient B. Expressed by the line represented by the equation that best matches the input variable x to the output variable y.
For example: y = B0 + B1 * x . We predict y for the given input x. The goal of the linear regression learning algorithm is to find the values ​​of B0 and B1.
Different techniques can be used for linear regression models. For example, the ordinary least squares method of linear algebra, and the gradient descent optimization algorithm. Linear regression has been around for more than 200 years and has been extensively studied. According to experience, this algorithm can well eliminate similar data and remove noise in the data. It is the fastest and simpler algorithm of choice.
2. Logistic regression
Logistic regression is another machine learning algorithm borrowed from the statistical field.
Same as linear regression. Its purpose is to find the corresponding parameter value for each input variable. The difference is that the transform used to predict the output is a nonlinear function called the logisTIc function.
The logisTIc function is like a big S. It converts all values ​​to a number between 0 and 1. This is useful, we can convert the output of the logisTIc function to 0 or 1 according to some rules (for example, 1 when it is less than 0.5). Then sort by this.
Because of this approach to model learning, the predictions made by logistic regression can be thought of as the probability values ​​of the two categorical data entered as 0 and 1. This is very useful in some questions that need to give reasonable predictions.
Just like linear regression, logistic regression can perform well in terms of features that need to be removed from output variables and similar features. It is a fast and efficient model for dealing with the two classification problem.
3. Linear discriminant analysis
Logistic regression is a traditional classification algorithm for binary classification problems. Linear Discriminant Analysis (LDA) is a better linear classification method if more classification is needed.
The interpretation of LDA is straightforward. It includes statistical properties for the input data for each class. For a single input variable, include:
Intraclass sample mean
Overall sample variable
Linear discriminant analysis
The prediction is performed by calculating the discriminant value of each class and based on the maximum value. This method assumes that the data is subject to a Gaussian distribution (bell curve). So it can better remove outliers in advance. It is a simple and effective way to predict problems for classification models.
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