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A sector that is currently being influenced by machine learning is the geospatial sector, through well-crafted algorithms that improve data analysis through mapping techniques such as image classification, object detection, spatial clustering, and predictive modeling, revolutionizing how we understand and interact with geographic information.
K-NearestNeighbor Regression Neural Network (KNN) The k-nearestneighbor (k-NN) algorithm is one of the most popular non-parametric approaches used for classification, and it has been extended to regression. DecisionTrees ML-based decisiontrees are used to classify items (products) in the database.
B BigData : Large datasets characterised by high volume, velocity, variety, and veracity, requiring specialised techniques and technologies for analysis. Data Wrangling: The cleaning, transforming, and structuring of raw data into a format suitable for analysis.
Overfitting: The model performs well only for the sample training data. If any new data is given as input to the model, it fails to provide any result. Decisiontrees are more prone to overfitting. Some algorithms that have low bias are DecisionTrees, SVM, etc. Variance: Variance is also a kind of error.
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