rrzta's Stars
rrzta/Machine-Learning---Case-Based-2
Explain, implement, analyze, and design unsupervised learning machine learning techniques, namely K-Means with the Water Treatment Plant dataset.
rrzta/Machine-Learning---Project-Based
Boosting is one of the methods contained in Ensemble Learning. This technique makes it possible to combine several models into one more robust model.
rrzta/Machine-Learning---Automated-Machine-Learning-TPOT
Add better insight into the use of AutoML for certain datasets, especially Breast Cancer Wisconsin (Diagnostic). In addition, it is expected to provide an understanding of the weaknesses and shortcomings of the selected use of AutoML, namely the Tree-Based Pipeline Optimization Tool (TPOT) for modeling automation.
rrzta/Machine-Learning---Case-Based-1
explain, implement, analyze, and design a supervised learning machine learning technique, namely Multilayer Perceptron (MLP) on the Audit Risk dataset.
rrzta/Machine-Learning---Learning
Nearest Neighbor is a simple algorithm that stores all available cases and classifies new cases based on a similarity measure. Using a non-parametric technique means that no parameters are set at the start.
rrzta/Machine-Learning---Reasoning
Reasoning using Fuzzy Logic. Fuzzy Logic relies on common sense to solve problems using degrees of truth between 0 and 1.
rrzta/Machine-Learning---Searching
We analyze the search for the minimum value of the function and domain (value limit) for 𝑥 and 𝑦 that have been provided. To analyze the search, a genetic algorithm is used.
rrzta/Tubes-Strategi-Algoritma-2022
TUGAS BESAR STRATEGI ALGORITMA 2022 - RAZITA AMALINA (1301200283); AUFA MUTIA (1301204233); ANDRI ZEFRINALDI (1301204255)