The learning tree sneads fl: The Learning Tree Early Education Center

The Learning Tree Early Education Center

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About the Provider

Description: The Learning Tree Early Education Center is a Child Care Facility in Sneads FL, with a maximum capacity of 103 children. The provider also participates in a subsidized child care program.

Additional Information: Provider First Licensed on: 3/7/07;

Program and Licensing Details

• License Number:
  C14JA0419
• Capacity:
  103
• Enrolled in Subsidized Child Care Program:
  Yes
• Type of Care:
  VPK Provider; After School;Before School;Drop In;Food Served;Full Day;Infant Care
• Initial License Issue Date:
  Mar 07, 2007
• District Office:
  Judicial Circuit 14
  383 Phillips Road
  Tallahassee, Florida 32308
• District Office Phone:
  (850) 778-4034 (Note: This is not the facility phone number.)
• Licensor:
  Crystal Higgins

Location Map

Inspection/Report History

Pinnacle Pointe Daycare Academy – Union City GA Child Care Learning Center

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Decision tree: what is it, what is its essence, types, advantages of the method

Decision tree has been used in various areas from customer service to machine learning. We tell you what problems this method solves in data analysis and how to build a decision tree.

• What is a decision tree
• Decision tree structure
• Where is the method applied?
• What tasks does the method solve
• Advantages and disadvantages of the method
• How to create a decision tree
• The main steps in building a decision tree
• Expert advice

What is a decision tree

A decision tree is one of the machine learning algorithms. The algorithm is based on the rule: “If , then “. For example:

If the subscriber pressed the number “1” after the voice greeting, then the call must be transferred to the sales department.

Decision trees are often used in the banking sector and in areas where scripts are used to communicate with customers and decision-making processes need to be managed. An example of such an area is financial services, where banks and insurance companies check customer information in a strict sequence to assess risks before entering into a contract.

When a bank employee receives a loan application, he follows the tree to decide whether to approve the application or not

Structure of a decision tree

A decision tree consists of nodes and leaves.

At the top of the tree is the initial root node into which the entire selection falls. Next, there is a check for the fulfillment of a condition or the presence of a sign. As a result of such a check, the data group is divided into subgroups: a subgroup of data that passed the test, and a subgroup of data that do not meet the specified condition.

Further data subgroups get to the next node with a new check. And so on to the final node of the task tree, which meets the given goal of data analysis or completes the decision-making process.

Leaves are end nodes with test results

Related material:

What is machine learning

Where the method is used companies, sales and customer service.

For example, the user did not complete the card payment through the bank application, he writes to the support service chat. The bank employee who answers the request will follow the algorithm: for example, the first thing he will ask is the payment ID. Further, the decision tree will branch depending on whether the user knows the identifier or not.

Scripts for the sales department are also most often based on the decision tree model: managers ask questions to a potential client and, depending on the answer, adjust the next question.

ISP Help Desk Operator Can Handle Calls Using Decision Tree Algorithm

In machine learning, statistics, and data analysis, Decision Trees can be used to make predictions, describe data, divide data into groups, and find relationships between them.

A simple and popular problem is binary classification. That is, the division of the set of elements into two groups, where, for example:

1 – success, yes, the answer is correct, the user returned the loan;
0 – failure, no, the answer is incorrect, the user did not return the loan.

For example, based on meteorological observations for the past 100 days, you need to predict whether it will rain tomorrow. To do this, you can divide all days into two groups, where:

1 – the next day it was raining;
0 – It didn’t rain the next day.

You can analyze a set of characteristics of each day: average temperature, humidity, whether it has rained in the past two weeks. The decision tree algorithm will search the total amount of data for those repeated conditions by which it is easiest to divide all days into “1” and “0”‎. Such conditions increase the likelihood of the desired result.

For example, it rained 50 days out of 100. Of these 50 rainy days, 40 of those rainy days also rained the next day. It turns out the forecast: if it rains today, then tomorrow with a probability of 80% it will also be

It’s not easy to figure out how to analyze data using a decision tree on your own. It is even more difficult to learn how to correctly interpret and present the results of your research. Students of the Data Analyst course develop these skills by solving real cases from different professional fields.

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What problems does the method solve?

In machine learning and data analytics, a decision tree is used to: and subcategories.

2. Determine the most significant conditions
The decision tree algorithm helps to evaluate the importance of a feature (from English feature importance). That is, to find such conditions that are most important for the given purpose of the study. Such conditions are closest to the beginning of the division of the main sample. If you build 100 trees to solve the same problem, then, most likely, at the beginning of these trees there will be the same conditions.

3. Increase the reliability of the result
A decision tree helps to form the most suitable sample for all conditions or make the most accurate forecast based on the available data.

An example of solving a problem using a decision tree

The task is to make a classifier of points that belong to circle number 1 or number 2.

For clarity, you can use Jupyter Notebook, an interactive development environment and a tool for data analysis. This will help visualize how the decision tree works.

Visualization of the problem in Jupyter Notebook: two circles whose points need to be classified, and data on these points

A circle centered at the origin can be described by a simple equation of the form:

x² + y² = R².

With this expression, you can reflect the relationship between the x and y coordinates and uniquely determine the relationship of a point to a circle of 1 or 2.

This is how the model with point data will look when using a decision tree of limited depth in three divisions

You can see the split for each individual factor, but the model will have a clear error – three splits form three lines.

How the model will look on the delayed selection

That is, in this task it is not enough to select the inner circle, because you will get a sector of points that is erroneously assigned to the outer circle.

If you visualize the separating surface in Jupyter Notebook, you can see the problem

This problem can be easily solved by adding a third variable, the sum of the squares of the x and y values.

This is how the data on points with the third variable will look like

After building the model on the generated data, you can see the result of the model on the new data.

Now, for the model, one division on the variable r is sufficient for unambiguous division

Advantages and disadvantages of the method

Advantages one attribute, so you can easily interpret the results and quickly find conditions that influenced them the most. For example, why a bank employee refused a loan to an applicant: because of age, lack of a certificate confirming income, or past due payments on past loans.

 from sklearn.datasets import load_iris 
 from sklearn import tree 
 # Load in our dataset 
 iris_data = load_iris() 
 
 # Initialize our decision tree object tree induction + pruning) 
 classification_tree = classification_tree.fit(iris_data.data, iris_data.target) 
 

 import graphviz 
 dot_data = tree.export_graphviz(classification_tree, out_file=None, 
 feature_names=iris.feature_names, 
 class_names=iris.target_names, 
 filled=True, rounded=True, 
 special_characters=True) 
 graph = graphviz. Source(dot_data) 
 graph.render("iris")