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    https://sophisticatedspectra.com/article/drosia-serenity-a-modern-oasis-in-the-heart-of-larnaca.2521391.html

    DROSIA SERENITY
    A Premium Residential Project in the Heart of Drosia, Larnaca

    ONLY TWO FLATS REMAIN!

    Modern and impressive architectural design with high-quality finishes Spacious 2-bedroom apartments with two verandas and smart layouts Penthouse units with private rooftop gardens of up to 63 m² Private covered parking for each apartment Exceptionally quiet location just 5–8 minutes from the marina, Finikoudes Beach, Metropolis Mall, and city center Quick access to all major routes and the highway Boutique-style building with only 8 apartments High-spec technical features including A/C provisions, solar water heater, and photovoltaic system setup.
    Drosia Serenity is not only an architectural gem but also a highly attractive investment opportunity. Located in the desirable residential area of Drosia, Larnaca, this modern development offers 5–7% annual rental yield, making it an ideal choice for investors seeking stable and lucrative returns in Cyprus' dynamic real estate market. Feel free to check the location on Google Maps.
    Whether for living or investment, this is a rare opportunity in a strategic and desirable location.

    Tree-based Machine Learning Algorithms: Decision Trees, Random Forests, and Boosting

    Posted By: naag
    Tree-based Machine Learning Algorithms: Decision Trees, Random Forests, and Boosting

    Tree-based Machine Learning Algorithms: Decision Trees, Random Forests, and Boosting
    English | 2017 | ASIN: B0756FGJCP | 107 pages | AZW3 | 0.4 Mb

    Get a hands-on introduction to building and using decision trees and random forests. Tree-based machine learning algorithms are used to categorize data based by known outcomes in order to facilitate predicting outcomes in new situations.

    You will learn not only how to use decision trees and random forests for classification and regression, and their respective limitations, but also how the algorithms that build them work. Each chapter introduces a new data concern and then walks you through modifying the code, thus building the engine just-in-time. Along the way you will gain experience making decision trees and random forests work for you.

    Table of Contents:

    A brief introduction to decision trees
    Chapter 1: Branching - uses a greedy algorithm to build a decision tree from data that can be split on a single attribute.

    Chapter 2: Multiple Branches - examines several ways to split data in order to generate multi-level decision trees.

    Chapter 3: Continuous Attributes - adds the ability to split numeric attributes using greater-than.

    Chapter 4: Pruning - explore ways of reducing the amount of error encoded in the tree.

    Chapter 5: Random Forests - introduces ensemble learning and feature engineering.

    Chapter 6: Regression Trees - investigates numeric predictions, like age, price, and miles per gallon.

    Chapter 7: Boosting - adjusts the voting power of the randomly selected decision trees in the random forest in order to improve its ability to predict outcomes.