A new study presents a machine learning model that accurately predicts the compressive strength of high-strength concrete, ...

In this paper, we investigate architectural characteristics of embedded systems for filtering high-volume sensor data before further processing. In particular, we investigate implementations of ...

Talekar, B. (2020) A Detailed Review on Decision Tree and Random Forest. Bioscience Biotechnology Research Communications, 13, 245-248.

Decision-Trees-and-Random-Forests import pandas as pd import numpy as np from sklearn.model_selection import train_test_split, cross_val_score from sklearn.tree import DecisionTreeClassifier, ...

Supervised Machine Learning using SciKit and other tools to do PCA, SVM, random forests, etc. for facial recognition and predictive decision making. The ML-GYM repository showcases machine learning ...

Reduces Overfitting: It helps reduce overfitting-the basic problem associated with Decision Trees. Disadvantages of Random Forest Less Interpretable: An ensemble nature makes it harder to understand ...

There are many other techniques for binary classification, but using a decision tree is very common and the technique is considered a fundamental machine learning skill for data scientists. There are ...

In this article, we propose the intuitionistic fuzzy random forest (IFRF), a new random forest ensemble of intuitionistic fuzzy decision trees (IFDT). Such trees in forest use intuitionistic fuzzy ...