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H2O.ai

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H2O is an Open Source, Distributed, Fast & Scalable Machine Learning Platform: Deep Learning, Gradient Boosting (GBM) & XGBoost, Random Forest, Generalized Linear Modeling (GLM with Elastic Net), K-Means, PCA, Generalized Additive Models (GAM), RuleFit, Support Vector Machine (SVM), Stacked Ensemble...



Introduction to Binary Classification with PyCaret - KDnuggets

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PyCaret is an open-source, low-code machine learning library in Python that automates machine learning workflows. It is an end-to-end machine learning and model management tool that speeds up the experiment cycle exponentially and makes you more productive. In comparison with the other open-source machine learning libraries, PyCaret is an alternate low-code library that can be used to replace hundreds of lines of code with few lines only. This makes experiments exponentially fast and efficient. PyCaret is essentially a Python wrapper around several machine learning libraries and frameworks such as scikit-learn, XGBoost, LightGBM, CatBoost, spaCy, Optuna, Hyperopt, Ray, and a few more.


[100%OFF] Machine Learning & Deep Learning in Python & R

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Learn how to solve real life problem using the Machine learning techniques Machine Learning models such as Linear Regression, Logistic Regression, KNN etc. Advanced Machine Learning models such as Decision trees, XGBoost, Random Forest, SVM etc. Understanding of basics of statistics and concepts of Machine Learning How to do basic statistical operations and run ML models in Python Indepth knowledge of data collection and data preprocessing for Machine Learning problem How to convert business problem into a Machine learning problem Can I get a certificate after completing the course? Are there any other coupons available for this course? Note: 100% OFF Udemy coupon codes are valid for maximum 3 days only. Look for "ENROLL NOW" button at the end of the post. Disclosure: This post may contain affiliate links and we may get small commission if you make a purchase.


Parallel XGBoost with Dask in Python

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Out of the box, XGBoost cannot be trained on datasets larger than your computer memory; Python will throw a MemoryError. This tutorial will show you how to go beyond your local machine limitations by leveraging distributed XGBoost with Dask with only minor changes to your existing code. Here is the code we will use if you want to jump right in. By default, XGBoost trains models sequentially. This is fine for basic projects, but as the size of your dataset and/or ML model grows, you may want to consider running XGBoost in distributed mode with Dask to speed up computations and reduce the burden on your local machine.


XGBOOST -- IN A NUTSHELL

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XGBoost stands for "Extreme Gradient Boosting". It is a decision tree-based algorithm which is used in Machine Learning. XGBoost makes use of the gradient boosting framework. Decision Trees are structures that consists of a set of leaf nodes, branches as well as internal nodes. Each leaf node represents a Class Label. The internal node represents the attributes while the branches connect the leaves to these internal nodes.


Efficient Batch Homomorphic Encryption for Vertically Federated XGBoost

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More and more orgainizations and institutions make efforts on using external data to improve the performance of AI services. To address the data privacy and security concerns, federated learning has attracted increasing attention from both academia and industry to securely construct AI models across multiple isolated data providers. In this paper, we studied the efficiency problem of adapting widely used XGBoost model in real-world applications to vertical federated learning setting. State-of-the-art vertical federated XGBoost frameworks requires large number of encryption operations and ciphertext transmissions, which makes the model training much less efficient than training XGBoost models locally. To bridge this gap, we proposed a novel batch homomorphic encryption method to cut the cost of encryption-related computation and transmission in nearly half. This is achieved by encoding the first-order derivative and the second-order derivative into a single number for encryption, ciphertext transmission, and homomorphic addition operations. The sum of multiple first-order derivatives and second-order derivatives can be simultaneously decoded from the sum of encoded values. We are motivated by the batch idea in the work of BatchCrypt for horizontal federated learning, and design a novel batch method to address the limitations of allowing quite few number of negative numbers. The encode procedure of the proposed batch method consists of four steps, including shifting, truncating, quantizing and batching, while the decoding procedure consists of de-quantization and shifting back. The advantages of our method are demonstrated through theoretical analysis and extensive numerical experiments.


Regression in Python using Sklearn, XGBoost and PySpark

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In the above story, we have used a Fitbit dataset. Based on the EDA, it was found that steps taken and calories are somewhat linearly correlated and together they may be indicative of a lower risk for all-cause mortality. More interestingly, among our data there is one dataset which has not been used yet which is a weight and BMI log. These data have a distinct nature since they are not necessarily machine generated, thereafter they serve the purpose of being'labels'. In simple words, users are collecting data regarding their activity using their Fitbit, and once in a while, they log some body information such as weight, fat and BMI.


Ensemble Machine Learning in Python: Random Forest, AdaBoost

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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. Imagine a world with drastically reduced car accidents, simply by removing the element of human error.


Sarus just released DP-XGBoost

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XGBoost is one of the most popular gradient boosted trees library and is featured in many winning solutions on Kaggle competitions. It's written in C and useable in many languages: Python, R, Java, Julia, or Scala. It can run on major distributed environments (Kubernetes, Apache Spark, or Dask) to handle datasets with billions of examples. XGBoost is often used to train models on sensitive data. Since it comes with no privacy guarantee, one can show that personal information may remain in the model weights.