Naive Bayes (ML_CN_5) This function uses the Bayes' theorem to calculate the probability of each class based on the frequency of the features in the training data, and classifies new input data based on highest probability
Naive Bayes is a probabilistic machine learning algorithm that uses Bayes' theorem to make predictions. It assumes that the features are independent of each other and calculates the probability of each class based on the frequency of the features in the training data.
Once the probabilities of each class are calculated, new input data is classified based on the class with the highest probability.
Sample Request
Build a Naive Bayes model named, "ClassicModel"
Copy {
"project_id" : 1 ,
"parent_id" : 7 ,
"block_id" : 8 ,
"function_code" : "ML_CN_5" ,
"args" : {
"model_name" : "NaiveModel" ,
"type" : "gaussian"
}
}
Building a Naive Bayes model
Naive Bayes
POST
https://autogon.ai/api/v1/engine/start
Request Body
200 Statemanagement object
Copy {
"status" : "true" ,
"message" : {
"id" : 8 ,
"project" : 1 ,
"block_id" : 8 ,
"parent_id" : 7 ,
"dataset_url" : "" ,
"x_value_url" : "" ,
"y_value_url" : "" ,
"x_train_url" : "" ,
"y_train_url" : "" ,
"x_test_url" : "" ,
"y_test_url" : "" ,
"output" : "{\"ClassicModel\": {\"function_code\": \"ML_R_3\", \"model_url\": ""}}"
}
}
Python Node
Copy const project_id = 1
const parent_id = 7
const block_id = 8
naiveBayes = await client .naive_bayes (project_id , parent_id , block_id , {
model_name : "ClassicModel" ,
});
Sample Request
Make predictions with the pre-built model passing an optional test data.
Copy {
"project_id" : 1 ,
"parent_id" : 8 ,
"block_id" : 9 ,
"function_code" : "ML_CN_5_P" ,
"args" : {
"model_name" : "ClassicModel" ,
"test_data" : ""
}
}
Predicting with Naïve Bayes
Naive Bayes Predict
POST
https://autogon.ai/api/v1/engine/start
Request Body
200: OK Statemanagement object
Copy {
"status" : "true" ,
"message" : {
"id" : 9 ,
"project" : 1 ,
"block_id" : 9 ,
"parent_id" : 8 ,
"dataset_url" : "" ,
"x_value_url" : "" ,
"y_value_url" : "" ,
"x_train_url" : "" ,
"y_train_url" : "" ,
"x_test_url" : "" ,
"y_test_url" : "" ,
"output" : "{\"y_pred_url\": ""}"
}
}
Python Node
Copy const project_id = 1
const parent_id = 7
const block_id = 8
naiveBayesPredict = await client .naive_bayes_predict (project_id , parent_id , block_id , {
model_name : "ClassicModel" ,
test_data : ""
});
Sample Request
Evaluate model metrics
Copy {
"project_id" : 1 ,
"parent_id" : 8 ,
"block_id" : 9 ,
"function_code" : "ML_CN_5_M" ,
"args" : {
"model_name" : "ClassicModel"
}
}
Naive Bayes Metrics
POST
https://autogon.ai/api/v1/engine/start
Request Body
200: OK StateManagement object
Copy {
"status" : "true" ,
"message" : {
"id" : 1 ,
"project" : 12 ,
"block_id" : 10 ,
"parent_id" : 11 ,
"dataset_url" : "" ,
"x_value_url" : "" ,
"y_value_url" : "" ,
"x_train_url" : "" ,
"y_train_url" : "" ,
"x_test_url" : "" ,
"y_test_url" : "" ,
"output" : "{'confusion_matrix': '', 'accuracy': 0.9}"
}
}
Python Node
Copy const project_id = 1
const parent_id = 7
const block_id = 8
naiveBayesMetrics = await client .naive_bayes_metrics (project_id , parent_id , block_id , {
model_name : "SimpleModel" ,
});