Python Artificial Intelligence Projects for Beginners

9.5
 and age lower than 
9.5
:
Repeat the process of splitting the data into two new subsets until there are no good splits,
or no remaining attributes, and leaf nodes are formed instead of question nodes. Before we
start with our prediction model, let us know a little more about the scikit-learn package.

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Building Your Own Prediction Models
Chapter 1
[ 14 ]
Common APIs for scikit-learn classifiers
In this section, we will be learn how to create code using the scikit-learn package to build
and test decision trees. Scikit-learn contains many simple sets of functions. In fact, except
for the second line of code that you can see in the following screenshot, which is specifically
about decision trees, we will use the same functions for other classifiers as well, such as
random forests:
Before we jump further into technical part, let's try to understand what the lines of code
mean. The first two lines of code are used to set a decision tree, but we can consider this as
not yet built as we have not pointed the tree to any trained set. The third line builds the tree
using the 
GJU
 function. Next, we score a list of examples and obtain an accuracy number.
These two lines of code will be used to build the decision tree. After which, we predict
function with a single example, which means we will take a row of data to train the model
and predict the output with the survived column. Finally, we runs cross-validation,
splitting the data and building an entry for each training split and evaluating the tree for
each testing split. On running these code the result we have are the scores and the we
average the scores.
Here you will have a question: 
When should we use decision trees?
 The answer to this can be
quite simple as decision trees are simple and easy to interpret and require little data
preparation, though you cannot consider them as the most accurate techniques. You can
show the result of a decision tree to any subject matter expert, such as a Titanic historian
(for our example). Even experts who know very little about machine learning
would presumably be able to follow the tree's questions and gauge whether the tree is
accurate.

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Building Your Own Prediction Models
Chapter 1
[ 15 ]
Decision trees can perform better when the data has few attributes, but may perform poorly
when the data has many attributes. This is because the tree may grow too large to be
understandable and could easily overfit the training data by introducing branches that are
too specific to the training data and don't really bear any relation to the test data created,
this can reduce the chance of getting an accurate result. As, by now, you are aware of the
basics of the decision tree, we are now ready to achieve our goal of creating a prediction
model using student performance data.