Long story short in this post we are dealing with predictions from a Combination of multiple models.
Each base model can be created using different splits of the same training dataset and same algorithm, or using the same dataset with different algorithms, or any other method.
Majority Voting :
There are various types of algorithm available named ID3, C4.5, CART, CHAID, QUEST, GUIDE, CRUISE, and CTREE. Here we are looking to three most commonly used one.
Following up from our previous article on Decision Tree, here we have few commonly used techniques.
Starting from ID3
C4.5 Algorithm address…
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Central Limit Theorem states that if you have population with mean mu and standard deviation sigma, taking sufficiently large random samples from population with replacement would give distribution of sample means to be approximately Normal.
Tree based models are a part of Non-Parametric Algorithms that are good at dealing with no linear relationships. They are good for Classification and Regression both.
Decision Trees is a Supervised Algorithm .It can be used for both categorical and continuous input and output variables by partitioning the feature space into a number of smaller (non-overlapping) regions with similar response values using a set of splitting rules. …
Need of Pruning is to reduce overfitting of the Decision tree and make a happy place for test data. Let’s see how we can do this.
Pruning can be done in two ways :
(Early Stopping Rule)
(Grow the tree and then trim it, replace subtree by leaf node)
Let’s start with what they do and why we need them.
Impurity measures are used in Decision Trees just like squared loss function in linear regression. We try to arrive at as lowest impurity as possible by the algorithm of our choice.
Impurity is presence of more than one class in a subset of data.
So all below mentioned measures differ in formula but align in goal. Watch till the end to know secret highlights of this topic.
Hasn’t specifying the number of clusters in KNN and K-mediods been a pain, no worries because now we have Hierarchical clustering to save us from the mess. Another added advantage for this is ability to visualize the construction of clusters diagrammatically.
Let us first introduce tree based representation Dendograms which make things beautiful for Hierarchical clustering.
Here’s a refinement of it, below is algorithm for PAM(Partitioning Around Mediods)
Swap phase: 4. For each cluster search if any of the object of the cluster decreases the average dissimilarity coefficient; if it does, select the entity that decreases this coefficient the most as the…
This is a first type of algorithm in unsupervised data.
This is similar to KNN which assumes that similar observations are grouped together are distance- based algorithms with only difference being that in KNN we check that labels of K nearest neighbors and then decide corresponding label of our point while in K means we find that why K nearest neighbors are similar to the point in consideration…
Steps for Algorithm
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