🤓 Hey friends! Miss Neura here to tell you all about decision trees - one of the OG algorithms in machine learning!
These tree-like models might look intimidating at first glance. 😱But don't worry, I'm gonna break it down step-by-step so you can totally ace understanding decision trees! 😎
Here's the tea - decision trees are used for classification and regression tasks. 🍵 That means things like looking at a bunch of data and deciding whether a photo contains a cat or dog (classification) or predicting the price of a house based on its characteristics like size and location (regression).
The magic of decision trees is that they break down complex decisions into a series of simple yes/no questions. Each question leads to another node in the tree, bringing you closer to making a final prediction.🌳
Let's dive in and learn all about how decision trees grow from just a seed of data into beautiful branches of understanding! I'll cover some history, explain how they work, the key algorithms, and when these tree models work best. 🎍
Stay tuned, things are about to get decidedly tree-mendous! 😂
History of Decision Trees
You got it, here's an expanded history section with more researcher background:
Alright, let's start with some history on where decision trees came from. 🌳
Way back in the 1960s, researchers like Edward Hunt from England 🇬🇧 and Philip Marin from the USA 🇺🇸 first came up with the idea of using tree models to make decisions. Groundbreaking! 🤯
These pioneering guys were some of the OG data scientists, doing their research back before computers were even a thing! 👴🏻Doing complex math and algorithms by hand - can you imagine? 🧮
Then in the 1980s, this Australian researcher named Ross Quinlan took decision trees to the next level.💫 He was working at the University of Sydney and specialized in machine learning. 🎓
Quinlan developed a fancy algorithm called ID3 that could build decision trees from data. 🌱This enabled the trees to be automated and grown much quicker than by hand. 💨
ID3 was truly a game changer! But Quinlan wasn't done yet.🧐 In 1993, still at the University of Sydney, he rolled out an even better algorithm called C4.5. 🆕
C4.5 was like the new and improved remix of ID3.🌟 It handled way more types of data and added things like pruning branches to prevent overfitting. Overfitting is when a machine learning model fits the training data almost perfectly, but fails when it sees new data. 😱
So next time you're using a decision tree, thank pioneers like Hunt, Marin, and Quinlan for planting the seeds 🌰that grew into these powerful models! Their work paved the way for decision trees to still be useful today! 🙌
How it works
Alright, now that we've got some history down, let's talk about what makes up these decision trees! 🌳
At the very top is the root node. This is like the seed that the whole tree grows from! 🌱 Below that are a bunch of branches split up by questions. Each branch leads to an interior node where another question gets asked.🌿
The questions are things like "Is this photo in color? Yes or No" Then depending on the answer, you go down another branch!
This keeps happening at each interior node until you get to a leaf node at the end of a branch.🌷 That leaf node is the final classification or prediction!
So decision trees use this top down approach to go from the root to the leaves, using questions to split the data at each step.🌾
When it comes to picking which question to ask at each node, decision trees want to find the one that results in the purest split of data.🌀
Two popular ways to measure this purity are information gain and gini impurity.📐
Information gain measures how much info you gain about the target variable by splitting on a given attribute.🗒️ The higher the information gain, the better that attribute is for splitting.
Gini impurity is kind of the opposite. It measures how mixed up the data is after splitting.👫 The lower the gini impurity, the purer the split.
So if there's an attribute that results in really high information gain or really low gini impurity when splitting the data - that's the one the decision tree will ask about at that node! 🌈
Let's say we're building a decision tree to classify fruits. 🍎🍐🍊
When splitting on the attribute color, we find that dividing into red and not red fruits gives us a lot of info about the target variable. All the red fruits are apples or cherries, while the non-red fruits are lemons, bananas, etc. This results in high information gain.✅
On the other hand, splitting by shape results in lower information gain. The round and not round groups are more mixed in terms of the target fruit types. Not as helpful!❌
For gini impurity, let's look at sweetness. Splitting the fruits into sweet and not sweet results in pretty pure groups - the sweet fruits are mostly apples, mangos, etc while the not sweet ones are lemons, limes. This has a low gini impurity. 👍
But splitting by size gives higher gini impurity. Big and small fruits are mixed, with both sweet and not sweet types in each group. Not as pure! 👎
So in summary, the decision tree will pick coloring and sweetness to split on over shape and size, because they result in purer branches and help classify the fruits better! 🍉
Decision Tree Algorithms
Get hyped folks, we're diving into the details on ID3 and C4.5 - two classic algorithms that make decision trees possible! 🌳🤩
Let's start with ID3 since it came first. 🥇 This algorithm uses information gain to select which attribute to split on at each node. Remember, Information Gain measures how much info you gain about the target variable by splitting on a given attribute.🗒️ The higher the information gain, the better that attribute is for splitting.
It calculates information gain like this:
Gain(Attribute) = Entropy(Parent) - ∑🚫[(Children entropy) * (Fraction of children)]
Basically, it looks at the parent node and says "How messy are you?"🌀 Then it tries splitting it different ways to ask "Did this make it less messy?" 👍
If splitting on an attribute causes a big drop in messiness, that attribute has higher information gain! 📈
The algorithm is like:
😱 Measure parent node entropy
🔀 Try different attribute splits
🤔 Calculate information gain from each split
👍Pick attribute with highest gain!
Next up is C4.5! 🆕 This algorithm leveled up ID3 with some cool improvements:
🔺 Uses gain ratio instead of information gain:
Gain Ratio(Attribute) = Gain(Attribute) / SplitInfo(Attribute) 🧮
See that split info part? That measures how many possible splits each attribute has.
Dividing the information gain by the split info balances things out. Now attributes aren't favored just for having more values.💡
Color has 3 possible values (red, green, blue)
Shape has 5 possible values (square, circle, etc)
Even if shape had higher information gain, color could have a better gain ratio if its gain is close and it has fewer splits.🤯
This avoids bias from attributes with lots of outcomes.👍
C4.5 is like:
😎 Calculate gain ratios
🌲 Grow initial tree
✂️ Prune useless branches
🔍 Use stats for missing data
💯 More accurate tree!
Phew, that's a lot of math! But hopefully it makes the core ideas behind these seminal decision tree algorithms clearer.
One of the best things about decision trees is how intuitive they are! 💡The flow chart structure makes it easy to explain the rationale behind the predictions.👍
Unlike some fancy black box models, you can clearly see how the tree divides up the data at each step. No mystery here! 🔍
Another advantage is flexibility. Decision trees can handle nonlinear relationships between features no problem. 📈 Even if the data is complex, the tree can model it through branching splits.🌳
Decision trees also work great with all data types. They can handle numerical, categorical, or text data in making splits.🌀 No need to encode or standardize anything first!
Some other pros are:
- Fast training and prediction times 🚄
- Works for regression and classification tasks 📝
- Performs embedded feature selection 💪
- Handles missing values ✅
So in summary, decision trees are intuitive, flexible models that work well right out of the box! The advantages make them a solid choice for your ML toolkit. 👩💻
Along with the many advantages, decision trees do come with some disadvantages to be aware of.
One issue is they can easily overfit the training data without proper regularization.🌳 Too many branches with too many specific splits can reduce generalizability.😬
That's where pruning comes in! Cutting off unnecessary branches prevents overfitting and improves performance on new data.✂️
Another downside is sensitivity to small variations in the data. 📉 Even small changes to the training data can result in a very different tree structure.
This can lead to high variance where different trees make different predictions. Not ideal for stability and consistency.💥
Some other limitations are:
- Performance degrades with too many features 📊
- Categorical variables limited to binary splits 🔀
- Struggles with handling redundant/irrelevant features ❌
- Doesn't perform as well with small datasets 📉
The key is being aware of these disadvantages and taking steps to address them where possible through techniques like pruning, ensemble methods, etc. 💪
Here are some common applications where decision trees excel:
Medical Diagnosis ⚕️
Decision trees can classify patients based on symptoms and help diagnose diseases. Very interpretable for doctors!
Credit Scoring 💸
Banks use decision trees to determine good candidates for loans based on financial history and traits.
Sentiment Analysis 😀😕😡
Identify positive, negative or neutral sentiment in text like product reviews using decision trees on the wording.
- Fraud detection - identify suspicious transactions 💳
- Pattern recognition - handwriting or image recognition ✍️
- Recommender systems - suggest products based on customer attributes 🛒
- Churn modeling - predict customers likely to cancel service ❌
The benefits like interpretability, handling various data types, and nonlinear relationships make decision trees a versatile choice for many real-world use cases!
Here's a TL;DR and vocab list to summarize the key points about decision trees:
Decision trees are intuitive ML models that use branching rules to make predictions. They split data recursively based on info gain/gini impurity to classify examples. Advantages include interpretability and handling nonlinearity. But they can overfit without pruning. Overall, a versatile algorithm useful for many applications!
Root Node - The starting point of the tree
Splitting Criteria - Metric like info gain used to split nodes
Pruning - Removing unnecessary branches to prevent overfitting
Entropy - Messiness or impurity in a dataset
Information Gain - Reduction in entropy after splitting on an attribute
Gini Impurity - Metric indicating how mixed the classes are
ID3 - Classic decision tree algorithm that uses information gain
C4.5 - Extension of ID3 with improvements like pruning
Overfitting - When a model performs worse on new data