Algorithm
Random forest
Description
The algorithm is built upon a decision tree to improve the accuracy drastically. Random forest generates many times simple decision trees and uses the ‘majority vote’ method to decide on which label to return. For the classification task, the final prediction will be the one with the most vote; while for the regression task, the average prediction of all the trees is the final prediction.
Type
Regression
Classification
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Random forest
Description
The algorithm is built upon a decision tree to improve the accuracy drastically. Random forest generates many times simple decision trees and uses the ‘majority vote’ method to decide on which label to return. For the classification task, the final prediction will be the one with the most vote; while for the regression task, the average prediction of all the trees is the final prediction.
Type
Regression
Classification
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👍5
Algorithm
AdaBoost
Description
Classification or regression technique that uses a multitude of models to come up with a decision but weighs them based on their accuracy in predicting the outcome
Type
Regression
Classification
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AdaBoost
Description
Classification or regression technique that uses a multitude of models to come up with a decision but weighs them based on their accuracy in predicting the outcome
Type
Regression
Classification
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👍4
Algorithm
Gradient-boosting trees
Description
Gradient-boosting trees is a state-of-the-art classification/regression technique. It is focusing on the error committed by the previous trees and tries to correct it.
Type
Regression
Classification
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Gradient-boosting trees
Description
Gradient-boosting trees is a state-of-the-art classification/regression technique. It is focusing on the error committed by the previous trees and tries to correct it.
Type
Regression
Classification
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👍3
Unsupervised learning
In unsupervised learning, an algorithm explores input data without being given an explicit output variable (e.g., explores customer demographic data to identify patterns)
You can use it when you do not know how to classify the data, and you want the algorithm to find patterns and classify the data for you
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In unsupervised learning, an algorithm explores input data without being given an explicit output variable (e.g., explores customer demographic data to identify patterns)
You can use it when you do not know how to classify the data, and you want the algorithm to find patterns and classify the data for you
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Algorithm Name
K-means clustering
Description
Puts data into some groups (k) that each contains data with similar characteristics (as determined by the model, not in advance by humans)
Type
Clustering
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K-means clustering
Description
Puts data into some groups (k) that each contains data with similar characteristics (as determined by the model, not in advance by humans)
Type
Clustering
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Algorithm Name
Gaussian mixture model
Description
A generalization of k-means clustering that provides more flexibility in the size and shape of groups (clusters)
Type
Clustering
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Gaussian mixture model
Description
A generalization of k-means clustering that provides more flexibility in the size and shape of groups (clusters)
Type
Clustering
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Algorithm Name
Hierarchical clustering
Description
Splits clusters along a hierarchical tree to form a classification system.
Can be used for Cluster loyalty-card customer
Type
Clustering
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Hierarchical clustering
Description
Splits clusters along a hierarchical tree to form a classification system.
Can be used for Cluster loyalty-card customer
Type
Clustering
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👍4
Algorithm Name
Recommender system
Description
Help to define the relevant data for making a recommendation.
Type
Clustering
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Recommender system
Description
Help to define the relevant data for making a recommendation.
Type
Clustering
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👍5
Algorithm Name
PCA/T-SNE
Description
Mostly used to decrease the dimensionality of the data. The algorithms reduce the number of features to 3 or 4 vectors with the highest variances
Type
Dimension Reduction
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PCA/T-SNE
Description
Mostly used to decrease the dimensionality of the data. The algorithms reduce the number of features to 3 or 4 vectors with the highest variances
Type
Dimension Reduction
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Concatenate DataFrames in Python
https://www.pythonforbeginners.com/basics/concatenate-dataframes-in-python
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https://www.pythonforbeginners.com/basics/concatenate-dataframes-in-python
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🍾4
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Telepathy.
An OSINT toolkit for investigating Telegram chats. Developed by Jordan Wildon.
https://github.com/jordanwildon/Telepathy
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An OSINT toolkit for investigating Telegram chats. Developed by Jordan Wildon.
$ pip3 install telepathyhttps://github.com/jordanwildon/Telepathy
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👍3