Mlearning.ai

FEBRUARY 2, 2023

Common causes of data leakage include using test data in the training process, using data from future time points, and using data that is not connected to the problem at hand. Data Leakage — Not using the appropriate test set  — Test set measures the generality of the model.

Machine Learning 52

Machine Learning Machine Learning ML ML 52

Pickl AI

FEBRUARY 12, 2024

Statistical Concepts A strong understanding of statistical concepts, including probability, hypothesis testing, regression analysis, and experimental design, is paramount in Data Science roles. However, there are a few fundamental principles that remain the same throughout. Here is a brief description of the same.

Data Science 52

Data Science Cross Validation Machine Learning Machine Learning 52

Towards AI

JULY 19, 2023

That post was dedicated to an exploratory data analysis while this post is geared towards building prediction models. Feel free to try other algorithms such as Random Forests, Decision Trees, Neural Networks, etc., among supervised models and k-nearest neighbors, DBSCAN, etc., among unsupervised models.

Machine Learning 40

Machine Learning Machine Learning K-nearest Neighbors Support Vector Machines 40

Heartbeat

SEPTEMBER 28, 2023

In a typical MLOps project, similar scheduling is essential to handle new data and track model performance continuously. Load and Explore Data We load the Telco Customer Churn dataset and perform exploratory data analysis (EDA). Random Forest Classifier (rf): Ensemble method combining multiple decision trees.

Support Vector Machines 52

Support Vector Machines Machine Learning Machine Learning ML 52

IBM Journey to AI blog

DECEMBER 20, 2023

Naïve Bayes algorithms include decision trees , which can actually accommodate both regression and classification algorithms. Random forest algorithms —predict a value or category by combining the results from a number of decision trees.

Machine Learning 82

Machine Learning Machine Learning Supervised Learning Clustering 82

PyImageSearch

DECEMBER 12, 2022

We went through the core essentials required to understand XGBoost, namely decision trees and ensemble learners. Since we have been dealing with trees, we will assume that our adaptive boosting technique is being applied to decision trees. For now, since we have 7 data samples, we will assign 1/7 to each sample.

Decision Trees 52

Decision Trees Deep Learning Deep Learning Exploratory Data Analysis 52

PyImageSearch

JANUARY 23, 2023

The reasoning behind that is simple; whatever we have learned till now, be it adaptive boosting, decision trees, or gradient boosting, have very distinct statistical foundations which require you to get your hands dirty with the math behind them. , you already know that our approach in this series is math-heavy instead of code-heavy.

Deep Learning 107

Deep Learning Deep Learning Algorithm Decision Trees 107

Pickl AI

JULY 10, 2023

This interactivity promotes exploratory data analysis and iterative development, making it suitable for data scientists and analysts. · Graphics and Data Visualization: R has robust capabilities for creating high-quality graphics and visualizations.

Data Science 52

Data Science Data Scientist Machine Learning Machine Learning 52

Heartbeat

MAY 29, 2023

It is therefore important to carefully plan and execute data preparation tasks to ensure the best possible performance of the machine learning model. It is also essential to evaluate the quality of the dataset by conducting exploratory data analysis (EDA), which involves analyzing the dataset’s distribution, frequency, and diversity of text.

Machine Learning 78

Machine Learning Machine Learning Natural Language Processing Data Preparation 78