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Summer's here, and you know what that means – picnic time!
Summer's here, and you know what that means – picnic time!
But with so many amazing spots in Boston, choosing the perfect one can feel overwhelming.
But with so many amazing spots in Boston, choosing the perfect one can feel overwhelming.
What if I told you there's a way to make picnic planning a breeze?
What if I told you there's a way to make picnic planning a breeze?
Decision Trees!
Decision Trees!
These powerful tools help us make decisions without the headache.
These powerful tools help us make decisions without the headache.
So, tell me, what do you crave for the most among these?
So, tell me, what do you crave for the most among these?
Sunshine 🌞 - Charles River
Sunshine 🌞 - Charles River
Shade 🌳 - Boston Common
Shade 🌳 - Boston Common
Nearby Ice Cream Stand 🍦 - J.P. Licks
Nearby Ice Cream Stand 🍦 - J.P. Licks
Waterfront 🌊 - Seaport
Waterfront 🌊 - Seaport
A Decision Tree works similarly!
A Decision Tree works similarly!
It breaks down a complex decision-making process into a series of simpler questions, just like how you would choose your picnic spot.
It breaks down a complex decision-making process into a series of simpler questions, just like how you would choose your picnic spot.
Here's a simple breakdown:
Here's a simple breakdown:
Root Node: The starting point (e.g., Is it sunny today?).
Root Node: The starting point (e.g., Is it sunny today?).
Decision Nodes: Where decisions are made based on features (e.g., Is there shade available?).
Decision Nodes: Where decisions are made based on features (e.g., Is there shade available?).
Leaf Nodes: The final decision (e.g., Perfect picnic spot!).
Leaf Nodes: The final decision (e.g., Perfect picnic spot!).
Each node represents a question about our data, and each branch is a possible answer, leading us to a decision.
Each node represents a question about our data, and each branch is a possible answer, leading us to a decision.
Consider it a series of "yes" or "no" questions to find the best picnic spot in Boston.
Consider it a series of "yes" or "no" questions to find the best picnic spot in Boston.
So, next time you’re basking in the summer sun, remember that Decision Trees are here to help simplify those tough choices – from picnics to predictions!
So, next time you’re basking in the summer sun, remember that Decision Trees are here to help simplify those tough choices – from picnics to predictions!
But wait, what could go wrong?
But wait, what could go wrong?
--> Instability: what if I make small changes to my data, will it be prone to a significant change in its predictions?
--> Instability: what if I make small changes to my data, will it be prone to a significant change in its predictions?
--> Accuracy: is one tree enough? Do we need a forest?
--> Accuracy: is one tree enough? Do we need a forest?
--> Bias: what if it is biased towards beaches, how do you manage bias in information gain in classification problems?
--> Bias: what if it is biased towards beaches, how do you manage bias in information gain in classification problems?
Get in touch at jain.van@northeastern.edu
Get in touch at jain.van@northeastern.edu
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