Cai Cai's Machine Learning Sklearn Class: Understandable from Scratch, from Model Invocation to Practical Implementation

The design of sklearn follows the "Swiss Army Knife" principle, integrating six core modules: Data Preprocessing: Similar to ...
C&EN

Efficient Implementation of Gaussian Process Regression Accelerated Saddle Point Searches with Application to Molecular Reactions

Article Views are the COUNTER-compliant sum of full text article downloads since November 2008 (both PDF and HTML) across all institutions and individuals. These metrics are regularly updated to ...
Hosted on MSN

Build Logistic Regression from Scratch in Python with Ease

Discover a smarter way to grow with Learn with Jay, your trusted source for mastering valuable skills and unlocking your full potential. Whether you're aiming to advance your career, build better ...
GitHub

Machine-learning-model-implementation

import pandas as pd import numpy as np import matplotlib.pyplot as plt from sklearn.model_selection import train_test_split from sklearn.linear_model import LogisticRegression from sklearn.metrics ...
Hosted on MSN

Multiple Linear Regression in Python from Scratch ¦ Explained Simply

In this video, we will implement Multiple Linear Regression in Python from Scratch on a Real World House Price dataset. We will not use built-in model, but we will make our own model. This can be a ...
Frontiers

Expectation-maximization alternating least squares for tensor network logistic regression

1 Department of Computer Science, Nagoya Institute of Technology, Aichi, Japan 2 RIKEN Center for Advanced Intelligence Project, Tokyo, Japan In recent years, a learning method for classifiers using ...
Scientific Research Publishing

Modeling Analysis of Chronic Heart Failure in Elderly People Based on Bayesian Logistic Regression Method

In Table 3, the VIF values for each variable are < 5, which has been reduced as multicollinearity between variables. 3.3. Use the Entropy Weight Method to Weight the Data When exploring the factors ...
IEEE

High Performance Computing Applied to Logistic Regression: A CPU and GPU Implementation Comparison

Abstract: We present a versatile GPU-based parallel version of Logistic Regression (LR), aiming to address the increasing demand for faster algorithms in binary classification due to large data sets.
Scientific Research Publishing

Machine Learning-Based Classification of Coronary Heart Disease: A Comparative Analysis of Logistic Regression, Random Forest, and Support Vector Machine Models ()

1 Department of Business Information System, Central Michigan University, Mount Pleasant, MI, USA. 2 Department of MPH, Central Michigan University, Mount Pleasant, MI, USA. 3 Department of ...