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Earth Engine Tutorial #32: Machine Learning with Earth Engine - Supervised Classification

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This tutorial shows you how to perform supervised classification (e.g., Classification and Regression Trees [CART]) in Earth Engine. The Classifier package handles supervised classification by traditional ML algorithms running in Earth Engine. The training data is a FeatureCollection with a property storing the class label and properties storing predictor variables. Class labels should be consecutive, integers starting from 0. If necessary, use remap() to convert class values to consecutive integers. The predictors should be numeric.


Conservation machine learning

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Ensemble techniques--wherein a model is composed of multiple (possibly) weaker models--are prevalent nowadays within the field of machine learning (ML). Well-known methods such as bagging [1], boosting [2], and stacking [3] are ML mainstays, widely (and fruitfully) deployed on a daily basis. Generally speaking, there are two types of ensemble methods, the first generating models in sequence--e.g., AdaBoost [2]--the latter in a parallel manner--e.g., random forests [4] and evolutionary algorithms [5]. AdaBoost (Adaptive Boosting) is an ML meta-algorithm that is used in conjunction with other types of learning algorithms to improve performance. The output of so-called "weak learners" is combined into a weighted sum that represents the final output of the boosted classifier.


Meta-Learning in Decision Tree Induction - Programmer Books

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The book focuses on different variants of decision tree induction but also describes the meta-learning approach in general which is applicable to other types of machine learning algorithms. The book discusses different variants of decision tree induction and represents a useful source of information to readers wishing to review some of the techniques used in decision tree learning, as well as different ensemble methods that involve decision trees. It is shown that the knowledge of different components used within decision tree learning needs to be systematized to enable the system to generate and evaluate different variants of machine learning algorithms with the aim of identifying the top-most performers or potentially the best one. A unified view of decision tree learning enables to emulate different decision tree algorithms simply by setting certain parameters. As meta-learning requires running many different processes with the aim of obtaining performance results, a detailed description of the experimental methodology and evaluation framework is provided.


Random Forest on GPUs: 2000x Faster than Apache Spark

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Disclaimer: I'm a Senior Data Scientist at Saturn Cloud -- we make enterprise data science fast and easy with Python, Dask, and RAPIDS. Check out a video walkthrough here. Random forest is a machine learning algorithm trusted by many data scientists for its robustness, accuracy, and scalability. The algorithm trains many decision trees through bootstrap aggregation, then predictions are made from aggregating the outputs of the trees in the forest. Due to its ensemble nature, random forest is an algorithm that can be implemented in distributed computing settings.


Ensemble Machine Learning in Python: Random Forest, AdaBoost

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Created by Lazy Programmer Inc. English [Auto-generated] Created by Lazy Programmer Inc. In recent years, we've seen a resurgence in AI, or artificial intelligence, and machine learning. Machine learning has led to some amazing results, like being able to analyze medical images and predict diseases on-par with human experts. Google's AlphaGo program was able to beat a world champion in the strategy game go using deep reinforcement learning. Machine learning is even being used to program self driving cars, which is going to change the automotive industry forever.


Identifying Vulnerable Households Using Machine–Learning

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We used machine-learning classification models (classification decision tree and random forest model) and applied to a household survey. This was …


Classification with Random Forests in Python

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The random forests algorithm is a machine learning method that can be used for supervised learning tasks such as classification and regression. The algorithm works by constructing a set of decision trees trained on random subsets of features. In the case of classification, the output of a random forest model is the mode of the predicted classes across the decision trees. In this post, we will discuss how to build random forest models for classification tasks in python. In this post, you'll see Classification with Random Forests in Python The random forests algorithm is a machine learning method that can be used for supervised learning tasks such as classification and regression.


A complete explanation of Random Forest Algorithm.

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Ensemble learning is a technique where there is a joining of different types of algorithm or same types of algorithm and then it forms a more powerful regression and classification model. Here, in the random forest algorithm, it combines with multiple decision trees and forms a model. Because of its diversity and simplicity, it is one of the most used algorithms. It is used for both classification and regression problems.


How to Develop a Bagging Ensemble with Python

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Bagging is an ensemble machine learning algorithm that combines the predictions from many decision trees. It is also easy to implement given that it has few key hyperparameters and sensible heuristics for configuring these hyperparameters. Bagging performs well in general and provides the basis for a whole field of ensemble of decision tree algorithms such as the popular random forest and extra trees ensemble algorithms, as well as the lesser-known Pasting, Random Subspaces, and Random Patches ensemble algorithms. In this tutorial, you will discover how to develop Bagging ensembles for classification and regression. How to Develop a Bagging Ensemble in Python Photo by daveynin, some rights reserved. Bootstrap Aggregation, or Bagging for short, is an ensemble machine learning algorithm. Specifically, it is an ensemble of decision tree models, although the bagging technique can also be used to combine the predictions of other types of models.


Machine Learning: Decision Trees

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This blog covers another interesting machine learning algorithm called Decision Trees and it's mathematical implementation. At every point in our life, we make some decisions to proceed further. Similarly, this machine learning algorithm also makes the same decisions on the dataset provided and figures out the best splitting or decision at each step to improve the accuracy and make better decisions. This, in turn, helps in giving valuable results. A decision tree is a machine learning algorithm which represents a hierarchical division of dataset to form a tree based on certain parameters.