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Information Gain measures how much a feature helps us to distinguish between different classes in a dataset. It's used to decide which feature to split the data on when building a decision tree.
If splitting a dataset into two groups based on a feature makes the groups more distinct from each other, that feature has high Information Gain.
Gini Impurity is a measure used to evaluate how mixed the labels are in a dataset. A Gini Impurity of 0 means all data points in the set have the same label (pure set). The goal is to reduce Gini Impurity when splitting data to make the subsets as pure as possible.
If a dataset has 90% of one class and 10% of another, the Gini Impurity will be higher than a dataset where all data points belong to the same class.
In a decision tree, a leaf node is where the decision-making process ends and a final prediction is made. A leaf is created when no further features provide useful information for splitting the data.
If after splitting on all features, the data in a node is still mixed, that node becomes a leaf where a decision or classification is made.
Building the best decision tree is challenging because making decisions based on immediate gains in Information Gain might not always lead to the best overall tree. There are various strategies and heuristics to build an optimal tree.
Using a greedy algorithm might not always give the best tree, so other methods like cross-validation or pruning are used to improve the tree.
In a decision tree, each internal node represents a feature used to split the data, and each leaf node represents a class label or final decision. The branches of the tree represent possible values of the features.
If you're deciding whether to play tennis, a decision tree might split on features like weather conditions, temperature, and humidity to make the final decision.
Pruning is a technique used to simplify decision trees by removing parts of the tree that don't provide significant predictive power. This helps to prevent overfitting and improve the model's performance on new data.
Imagine a decision tree that has grown too large and complex. Pruning removes some branches to make the tree simpler and more generalizable.
Decision trees can become too complex, leading to overfitting. Pruning can help simplify the tree. This process involves either cutting back the tree from the leaves or trimming it from the root.
A tree that fits every detail of the training data might not perform well on new data. Pruning helps by simplifying the tree to improve its performance.
A Random Forest is an ensemble model made up of many decision trees. Each tree is built using a random subset of features, and the final decision is made by aggregating the results from all the trees.
Instead of relying on a single decision tree, a Random Forest combines predictions from multiple trees to make a more accurate and reliable prediction.
Random Forests reduce overfitting by using multiple trees that each look at different subsets of data and features. This approach makes the model more robust and less likely to fit noise in the training data.
By averaging the predictions from many trees, Random Forests are less sensitive to the quirks of individual trees, leading to better generalization on new data.
When building a Random Forest, only a random subset of features is considered for each split in a tree. This randomness helps ensure that the forest does not overfit and improves overall model performance.
In each decision tree within the Random Forest, only some features are considered for each split, making each tree unique and improving the overall model.
Random Forests enhance model performance by training multiple decision trees on different subsets of data and features. Aggregating their predictions leads to a more accurate and stable model.
When classifying data, Random Forests use the majority vote from all the trees to decide the final classification, leading to more reliable results.
Bagging (Bootstrap Aggregating) is a technique used in Random Forests where multiple trees are trained on different random subsets of the data. The final prediction is made by combining the predictions from all trees.
In Random Forests, each tree is trained on a random subset of data, and the final decision is based on the majority vote of all the trees.
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