The following is a data set compromising of 4 parameters, which lead to the prediction of whether an elephant would be spotted or not?
The parameters which affect the prediction are –
• Outlook
• Temperature
• Humidity
• Wind
By looking at the table
Please answer the following -
Answer the following questions regarding the previous exercise:
1. Did you manage to draw the Decision Tree without any assistance?
2. Was it challenging for you to draw the decision tree for this dataset? If so, why?
3. Were all the parameters equally important for the Decision Tree? Did you notice any redundant data? If yes, what was it?
4. What if the dataset had more than 1000 data sets? Will decision tree still be a
suitable model for it? Why?
Answers
Hey mates here is your all answers....
1 .Seven Tips for Creating a Decision Tree
●Start the tree.
Draw a rectangle near the left edge of the page to represent the first node. In this rectangle, write the first question, main idea, or criterion that will lead to a decision.
●Add branches.
For every possible alternative draw a separate line that begins at the node and moves away toward the right of the page. Using a loan approval process as an example, the first node may have been "Income", and the associated branches might be <$50K, $51K - $100K, >$101K.
●Add leaves.
The bulk of the decision tree will be leaf nodes. At the end of each branch add a leaf node. Fill each of these leaf nodes with another question or criterion.
●Add more branches.
Repeat the process of adding a branch for each possible alternative leading from a leaf. Label each branch just as before.
●Complete the decision tree.
Continue adding leaves and branches until every question or criterion has been resolved and an outcome has been reached.
●Terminate a branch.
Continue adding leaves and branches until every question or criterion has been resolved and an outcome has been reached.
●verify accuracy.
Consult with all stakeholders to verify accuracy.
2 KA ANSWER
if you use decision stumps high bias family of models and you'll want to use boosting .
3 Ka ANSWER
Tree Based algorithms like Random Forest, Decision Tree, and Gradient Boosting are commonly used machine learning algorithms. Tree based algorithms are often used to solve data science problems. Every data science aspirant must be skilled in tree based algorithms. We conducted this skill test to help you analyze your knowledge in these algorithms.
4 Ka ANSWER
The problem is not your data set, it 's just matter of defining inside of the program, since the capacity of ram is limited and it doesn't support this data set. The solution can be separating into different data-set which seems to be boring and time consuming or using "tall array" and "matfile" in matlab to make it readable.
Using 10 billion features to train a prediction model with 100 inputs seems like an overkill. The data can be better utilized by splitting into multiple batches, training several models and ensembling them.
Hope this will help you please mark me as brainlist answer for my hard work and spend time to find answer from internet for short answers as you can easily remember......
Answer:
an Elephant would be spotted or not. The parameters which affect the prediction.
Explanation:
Outlook, Temperature, Humidity and Wind. ... If outlook = Sunny and Humidity = Normal, then Elephant Spotted = Yes.