A deep dive into neuton
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Neuton is a cloud-based platform enabling users to create high-performance machine learning models without prior knowledge of the subject. The model development process involves uploading a CSV dataset, automatic preprocessing, model training, and prediction through web access or API. Neuton offers detailed exploratory data analysis and model quality evaluation, providing insights into model performance and data relevance.
A deep dive into neuton
- 1. A Deep Dive into Neuton Angelica Lo Duca https://alod83.medium.com/
- 2. Overview of Neuton Neuton is a cloud-based platform, which allows the development of high-performance Machine Learning models without any knowledge of Machine Learning. Compared to other existing solutions, Neuton does not require any software or library installation.
- 3. The Three Steps for Model Development in Neuton DATASET 1. Dataset Upload 2. Automatic Preprocessing 3. Automatic Feature Engineering 4. Report on Exploratory Data Analysis TRAINING 1. Automatic Model Training 2. Report on Model Quality Evaluation PREDICTION 1. Web Prediction 2. REST API Access 3. Downloadable solution 4. Report on Model Obsolescence
- 4. Dataset The dataset must be a CSV file, where the first row must indicate the column names. The file format must be utf-8. It does not matter that the dataset has been already preprocessed with data preprocessing or feature engineering techniques. It is sufficient to upload a reasonably organized dataset, which may also contain missing values, duplicates, etc.
- 5. Target Variable Selection Task type Regression Binary Classification Multiclass Classification
- 7. Training An ad-hoc virtual machine is created in the cloud, to perform your training task. Training may take some time. However, there is a progress bar which indicates the percentage of completion. It is also possible to receive a message on the phone when the training operation is finished.
- 8. Prediction Prediction can be performed at three levels: ● Web Prediction ● REST API Access ● Downloadable solution Results of predictions can be either exported as a CSV file or analyzed in Neuton.
- 9. The Four Stages of Quality Evaluation in Neuton DATA Exploratory Data Analysis Provided Graphs: 1. Data Overview 2. Continuous Data 3. Discrete Data 4. Target Data 5. Correlations and Outliers 6. Missing Values TRAINING Model Quality Diagram A radar chart, with the following metrics: 1. MSE 2. RMSE 3. RMSLE 4. MAE 5. Мax AE 6. Мin AE 7. R² 8. RMSPE + Multiple Classification Metrics PREDICTION Confidence Interval Model-to-data Relevance Indicator The result of analysis is provided for each test sample and is shown as a table APPLICATION Historical Model-to-data Relevance Indicator Validate Model on New Data Manage model lifecycle