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In this lecture, we will learn how to create random forest classifier in Python.

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Not that we are only creating our classification, Morton, but you can also create do a regression

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model using the same method, but different codes for regression.

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Let's first look at the documentation of random photos on Escalon Web site.

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This one is for regression.

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So let's open the documentation for classifier.

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If you see the parameters or almost similar to one which we have for begging, the first one is the

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number of estimated.

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This is the number of Greece we want in our forest.

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So by default, this is spent.

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But we will lose tosing for our problem.

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Next is the criteria by default.

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It is GENI, but you can also change it to and Groppi for Cross and Groppi.

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As we have discussed with decrease.

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Next are some of the parameters for our KRI, so maximum depth, minimum sample displayed, minimum

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sample leaf.

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We have already discussed it during our D.C. entry tutorial.

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Now, this here is an important parameter.

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This is Max feature.

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Few remember, we select only a subset of our features to create separate trees in random forest.

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For example.

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In our case, we have 19 variables, so we will only take a subset of variables for each of the three.

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And by default, this is set to auto.

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And when we select auto, we get it squared Rudolph and feature selected.

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So we have 20 features.

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So a square root of 20 is around four point four.

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So probably we will get four or five features if we want.

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We can also give some numeric value or we can also provide a fraction of the maximum features.

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So, for example, if I give point five, we will select 10 features.

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So by default, the set to auto and we will also go with auto.

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Now, you can go through all the other parameters, but we will stick to before you consider.

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My third is almost similar.

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We will first import random forest classifier.

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We will create our random forest classifier object.

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We will train that object using the word ekstrand, inviting data, and then we will predict the values

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of by using a word to express data.

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So let's imported first and then we are using these parameters.

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We want those entries.

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So that's why we are giving an underscore to cemetaries thousand.

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We want the full processing power of war machine or computer.

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That's why we are giving and underscore the jobs equal to minus one by default.

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This is set to one that is our computer will only use one processor, but it is recommended that you

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use and jobs equal to minus one to get the full potential of your machine.

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Then we are giving random status for you to.

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Since we are randomly picking their time and randomly picking features for each of our Creat.

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So if we want to reproduce the same result in future, we can use random searches for to there to get

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the same result as this research.

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So always stick to some integer value of random.

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For example, we are using 42.

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But you can also use zero one, two or three.

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This will not impact you.

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Add more to the performance.

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This is just for reproducibility of the research that we are getting.

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And you already know that over M here is four or five.

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Since we are giving the default value and and default value, Biton is going to take on that group of

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20, which is equal to 4.5.

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Let's run this.

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And now we're in for to what extent and downward trend in this classified object.

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It will take some time.

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Now, let's grow confusion metrics on our test data.

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You can compare it with your bagging deserts.

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And then we're going to find out that Turistas Sports offered this model.

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So what accuracy for this model is sixty three point one percent.

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So if you compare the begging and begging of accuracy, score was six to 10 percent, but none for us.

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We have increased that accuracy score by one percent.

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So that's all for this video in the next video.

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We will learn how to optimize the values of our hyper parameters.

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So if you remember, there are different crede related.

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Parameters here, such as Max Thep minimums and police split in our example, we have used the default

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values, what we can optimize this value to get the best result for our data.

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So we will use Siwiec Grid Search and we will find the best tree for our data.