There is no free lunch in machine learning. Shortly, machine learning works best for every problem especially for the relevant supervised learning example predictive modeling. Such as, you cannot say that the neural network is better than decision trees or vice versa. Some of the elements like the structure of the dataset and size play a noticeable role.

It is essential to use a different algorithm for the issue whereas using the holdout test set to evaluate performance and choose the winner. Make sure to choose the appropriate algorithm for your problem that is where selecting the right machine learning task comes in. like an analogy, you need to use the broom, vacuum; mob to clean your house but you would not bust out a shovel and begin digging.

In this blog, you will come across the top machine learning algorithm you should know so here these are:

**Linear Regression**

It is one of the most well-understood and known algorithms in machine learning and statistics. It is better to use the predictive modeling that is mainly associated with making the most accurate predictions possible or minimizing the risk at the expense of explainingCyber-crooks ability. You can borrow, reuse and steal algorithms from the different fields that include statistics. The representation of the linear regression includes the equation that best fits to know the relationship of imputing variable (x) and the output variables (y),

**Logistics Regression**

It is another technique borrowed by machine learning from the field of statistics. It is the method for binary classification issues with two class values. Logistic regression is similar to linear regression in that the aim is to find the values for the coefficient that weigh each input variable. The prediction for the output is shifted using the nonlinear function known as the logistic function. It is considered useful due to the application of the rule to the output of the logistic function to snap values to 0 and 1.

**Linear Discriminant Analysis**

If you are opting for over two classes, the linear discriminant analysis algorithm is the best linear classification technique for you. In logistic regression, you are traditionally limited to use only two class classification problems. It consists of statistical properties of the data that are calculated for every class. Prediction in this technique is based on the discriminant value for every class and creating predictions for the class with the biggest value.

**Classification And Regression Trees**

It is an important kind of algorithm for predictive modeling machine learning; it is like the binary tree. This is the binary tree from data structures and algorithms, not too fancy. The leaf nodes of the tree comprise an output variable that is used to make predictions that are made by walking the splits of the tree until reaching the leaf note and output the class value at that leaf node.

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