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Data Science & Machine Learning
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“Join this channel to learn data science, artificial intelligence and machine learning with funny quizzes, interesting projects and amazing resources for free
For collaborations: @love_data”
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How do we know how many trees we need in random forest?
The number of trees in random forest is worked by n_estimators, and a random forest reduces overfitting by increasing the number of trees. There is no fixed thumb rule to decide the number of trees in a random forest, it is rather fine tuned with the data, typically starting off by taking the square of the number of features (n) present in the data followed by tuning until we get the optimal results.
How do we select the depth of the trees in random forest?
The greater the depth, the greater amount of information is extracted from the tree, however, there is a limit to this, and the algorithm even if defensive against overfitting may learn complex features of noise present in data and as a result, may overfit on noise. Hence, there is no hard thumb rule in deciding the depth, but literature suggests a few tips on tuning the depth of the tree to prevent overfitting:
• limit the maximum depth of a tree
• limit the number of test nodes
• limit the minimum number of objects at a node required to split
• do not split a node when, at least, one of the resulting subsample sizes is below a given threshold
• stop developing a node if it does not sufficiently improve the fit.
What are the main parameters of the random forest model?
max_depth: Longest Path between root node and the leaf
min_sample_split: The minimum number of observations needed to split a given node
max_leaf_nodes: Conditions the splitting of the tree and hence, limits the growth of the trees
min_samples_leaf: minimum number of samples in the leaf node
n_estimators: Number of trees
max_sample: Fraction of original dataset given to any individual tree in the given model
max_features: Limits the maximum number of features provided to trees in random forest model
How do we handle categorical variables in decision trees?
Some decision tree algorithms can handle categorical variables out of the box, others cannot. However, we can transform categorical variables, e.g. with a binary or a one-hot encoder.
When do we need to perform feature normalization for linear models? When it’s okay not to do it?
Feature normalization is necessary for L1 and L2 regularizations. The idea of both methods is to penalize all the features relatively equally. This can't be done effectively if every feature is scaled differently.
Linear regression without regularization techniques can be used without feature normalization. Also, regularization can help to make the analytical solution more stable, — it adds the regularization matrix to the feature matrix before inverting it.
What is the area under the PR curve? Is it a useful metric?
The Precision-Recall AUC is just like the ROC AUC, in that it summarizes the curve with a range of threshold values as a single score.
A high area under the curve represents both high recall and high precision, where high precision relates to a low false positive rate, and high recall relates to a low false negative rate.
What is bag of words? How we can use it for text classification?
Bag of Words is a representation of text that describes the occurrence of words within a document. The order or structure of the words is not considered. For text classification, we look at the histogram of the words within the text and consider each word count as a feature.
What is clustering? When do we need it?
Clustering algorithms group objects such that similar feature points are put into the same groups (clusters) and dissimilar feature points are put into different clusters.
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