This requires basic machine learning literacy — what kinds of problems can machine learning solve, and how to talk about those problems with data scientists. Linear regression and feature ...

Based on my experience, the three most important parameters to explore and modify are n_estimators, min_data_in_leaf and learning_rate. A LightGBM regression model is made up of n_estimators (default ...

Sparse data is still representing something within the variables. Missing data, however, means that the data points are unknown. Challenges in machine learning with sparse data. There are several ...

The diagram in Figure 2 gives you a rough idea of support vector regression for a scenario where there is just one predictor variable x. Each dot is a training data item. The red line is the linear ...

Data cleaning and Exploratory Data Analysis (EDA) might not seem glamorous, but the process is vital for guiding your real-world data projects. The chances are that you have heard of linear regression ...

I use Python 3 and Jupyter Notebooks to generate plots and equations with linear regression on Kaggle data. I checked the correlations and built a basic machine learning model with this dataset.

Machine learning uses algorithms to turn a data set into a model that can identify patterns or make predictions from new data. Which algorithm works best depends on the problem.

EHR data may be particularly suitable for machine learning (ML) techniques, as such algorithms can process high-dimensional data and capture nonlinear relationships between variables. By comparison, ...