The problem decision tree python is the best and most popular algorithm if you have classification and regression. The Decision tree python is like a probability tree for supervised machine learning algorithms. Like from the tree, the branches come out and spread all over. It happens in the decision tree python as well. The data from the main source is continuously splitting in the decision tree.
The dataset is split into subsets of data. The decision tree python has different nodes, just like the tree branches. While the data is continuously splitting the news, the Decision tree python is continuously becoming advanced.
We can take a real-life example of how the decision tree python functions.
So like you are planning to go to Goa next month, now you need to book a flight for that you look for the flight for selected if they are not available you would look for their dates. If the tickets are available, you look at the prices for the same. After that, if the prices are high, you would look somewhere else. All the branches are connected.
Some of the terms which are used in decision tree python are –
- Parent node – the root node is called the parent’s node
- Rode node – it is the node that starts the decision tree.
- Leaf node – the conclusion of the decision tree is the leaf node
- Splitting – the point the tree is split that I called splitting
- Child node – node which is not the parent node. It is called the child node.
R tree python
R tree python is the casing of python libspatialindex that arrange number that leads to the spatial indexing feature of the spatial curious python user.
The features of R Tree Python are –
- R tree python has a single root along with the internal and leaf nodes.
- The biggest spatial domain is located in the root.
- Parent nides accommodate the pointer toward the child nodes. The child nodes completely overlay the area of the parent nodes.
- The data of the minimum bounding region and the current objects are in the leaf nodes.
Implement Tree Decision Python
Some of the python packages are used to implement tree Decision python to execute the implementation further.
The python packages used are –
Sklearn
The package contains machine learning algorithms. Its modules are train_test_split.
NumPy
It is a python module for mathematical calculations. It is fast.
Panda
It is a python package that is used for reading in writing. With the help f data frames, data manipulation can be done quickly.
The assumption while using Tree Decision Python:
- From the beginning, the root is the base f the training.
- The second assumption is that the attribute is continuous. The attribute is categorical for information gain and the Gini index.
- The attribute values are assigned in a looping form.
- For attribute, the internal or root node is used in a statistical method.
Pseudocode
The finest attribute is placed on the tree’s root node. Now the data is split into the subsets datasets. The subsidy shall have the same value. Like each subset has a value of 4, all the subsets would have the same value. The leaf node is found in all branches by repeating 1 and 2 on every subset.
During the implementation of the tree decision, python goes under two phases- the building and operational phases. The building phase is the phase that preprocesses the dataset. Split the data into various subsets using sklearn. It also trains the classifier. The operational phase makes the prediction and calculates the accuracy.
Data Import
- For the manipulation and to import the data, use panda package python.
- The dataset needs to download the dataset to make it run on the system. While doing this, make sure to have a properly working internet connection.
- When the dataset is isolated “,” we need to pass the sep parameter’s value as,. “
- The header’s parameter value is none, and the dataset does not have any header. This step is crucial. Otherwise, the system would consider the first line the header.
Data slicing
- The data is separated into two sets – training and testing datasets. The separation should be done to be previously the training is done.
- Train_test_split is used in the sklearn module to separate the testing and training data sets.
- The attribute is separated from the target variable.
- X variable is the attribute, and y is the target variable.
- After splitting the target variable and attribute then, separate the testing and training datasets
- The datasets are split into the ratio of 70:30 . The ratio is between training and testing.
A term used in code
The n attribute is selected as the Gini index and information gain. Attributes are for the internal and root node.
How does the Python machine learning decision tree work
As discussed earlier, the python machine learning decision tree starts from the root node. The decision tree algorithm works like it compares the value of the root nid, not the other nodes, from one to another. It compares each node. After comparing with one node, it goes to the other node. The procedure continues until the final node or the leaf node has not been reached. These two methods from which the attributes are selected for the python machine learning decision tree –
Gini index
To select the impurity and purity of the decision tree, python during the creation of the tree in the form of a CART – Classification and Regression Tree. The attributes with the lower Gini index are given preference compared to the ones with a high Gini index. The binary splits are created using the CART algorithm. The calculation formula for the Gini index –
Gini Index= 1- ∑jPj2
Information gain
- The information gain is for calculating the feature in a class. Information is the analysis of changes done in entropy after the distribution of datasets in the attributes.
- The value of the information gain decides the separation of the nodes in the decision tree.
- There is always an effort to maximize the value of the information gained in the decision tree.
- Formula for calculating Information Gain= Entropy(S)- [(Weighted Avg) *Entropy(each feature)
Python tree tutorial
Here are some steps for the python tree tutorial
Step 1 – The initial step starts with the root node like X, which has the complete dataset.
Step 2 – Using ASM – attribute selection measure w will find the best attribute.
Step 3 – The splitting process starts here. The X is split into nodes according to the best attribute with possible values.
Step 4 – bring out the decision tree node with the best attribute.
Step 5- repeatedly keep on making new decision trees using the subsets. Repeat step 3 and keep on going with the procedure till the time you reach a conclusion which would be the leaf node
Conclusion
The decision tree python and r tree python are simple to understand in real life as well. The same procedure same as the python tree tutorial is done in real life as well by human beings. It is beneficial at the time of decision-making problems. It helps with possible outcomes for an issue. The requirement of data cleaning is not needed much compared to other algorithms.
