How To Use
APMC - HOW TO USE
Automatic predictive model constructor (APMC) allows to train and develop supervised machine learning models. Models are training based on data-set, which includes a set of predictors (independent variables) and one target value (dependent variable). When a model is trained allows predicting so far unknown target value based on any predictors.
More information and examples data sets in APMC documentation.
Example data sets:
for classification purposes:
for regression purposes:
--> USA Housing
How to use step-by-step:
1. Prepare your data
Data must be prepared in accordance with the following rules:
-> data set must be with “*.csv” extension
-> default delimiter type is “,” !
-> in case of float numbers use dots instead of commas
-> predictors are in columns
-> every column need a label name
-> last column is target values
NOTE 1: Maximum number of predictors is 10, maximum number of records is 10000.
NOTE 2: In case of classification models target value must be a string, in case of regression models target value must be a number.
2. Project name
Type name of your project, your project, model and a statistical report will be saved with this name
3. Select type of model:
APMC support classification and regression algorithms. If target values are quantitative variables (price, weight, height) select a regression model. If target value is a categorical variable (gender, species, blood type) select classification model.
Normalization may have a positive influence on model training, this function is optional.
When the user finishes above-mentioned steps and click "Pre-train" button.
6. Select model
After step no. 5, set of models are ready, the user needs to select which one will move on to the next stage of training. We suggest which algorithm probably is [for current data set] the best, based on the model`s metrics.
After click the "TRAIN" button, the model is prepared for use, when it is ready, the "PREDICT" button redirects to the page with the user's model collection.
8. Models collection
After the redirect, the user has a preview of the model's metrics, and the ability to predict target values.
9. Statistical report and tree graph
To get the statistical report, go to the "model" tab in the user panel, there is the download report button. If the selected algorithm is: random forest regression or random forest classification, it is also possible to download a tree graph.