Answers.Body: <p>You could formulate the problem of predicting disease severity as a classification one, you give the algorithm those 900 attributes and their corresponding labels (severe/not severe) after training, you give it a new data point with just the 900 attributes, it returns the correct label severe or not.</p>
  
  <p>There is an enormous number of algorithms in the ML literature for classification, some of them formulate the problem explicitly as learnable rules, i.e. let the machine figure out the rules given the attributes.</p>
  
  <ol>
  <li><a href="ftp://ftp.boulder.ibm.com/software/analytics/spss/support/Stats/Docs/Statistics/Algorithms/14.0/TREE-CART.pdf" rel="nofollow noreferrer">Classification and Regression Trees by Breiman et al (1984)</a></li>
  <li><a href="https://en.wikipedia.org/wiki/Random_forest" rel="nofollow noreferrer">Random forest by Ho in 1995</a></li>
  <li><a href="https://machinelearningmastery.com/gentle-introduction-xgboost-applied-machine-learning/" rel="nofollow noreferrer">XGBoost</a>  </li>
  </ol>
  
Answers.ContentLicense: CC BY-SA 4.0
Answers.CreationDate: 2018-05-16T06:59:38.300Z
Answers.Text: "it returns the correct label": not always.
Answers.UserDisplayName: pasaba por aqui
Answers.ContentLicense: CC BY-SA 4.0
Answers.CreationDate: 2018-05-16T07:00:43.053Z
Answers.Text: yes, I mean in a probabilistic way
Answers.UserDisplayName: Fadi Bakoura
Answers.ContentLicense: CC BY-SA 4.0
Answers.CreationDate: 2018-05-16T06:42:30.850Z
Answers.LastActivityDate: 2018-05-16T06:42:30.850Z
Answers.UserDisplayName: Fadi Bakoura
Body: <p>I have a medical dataset with 14000 rows dataset with 900 attributes. I have to predict disease severity using that. I would like to know whether we can write rules in python language for training an agent for medical diagnostic using machine learning.</p>
  
  <p>Can an agent make the decisions by the rules coded in python and that agent get trained with some machine learning algorithms? If so is there any agent architecture and model for the agent which is good in this context?</p>
  
  <p><em>Edit:</em> By the rule, I meant something like this.."if x>y output z as action". By the word "Training" I meant "how to tell this agent to do this action"?</p>
  
Tags: <machine-learning><python><datasets><intelligent-agent>
Title: Can we code rules for an agent in python language other than predicate calculus?
UserDisplayName: Dayz
ThreadText: # Can we code rules for an agent in python language other than predicate calculus?
  
  (Asked by: username_0 on 2018-05-16)
  
  I have a medical dataset with 14000 rows dataset with 900 attributes. I have to predict disease severity using that. I would like to know whether we can write rules in python language for training an agent for medical diagnostic using machine learning.
  
  Can an agent make the decisions by the rules coded in python and that agent get trained with some machine learning algorithms? If so is there any agent architecture and model for the agent which is good in this context?
  
  *Edit:* By the rule, I meant something like this.."if x>y output z as action". By the word "Training" I meant "how to tell this agent to do this action"?
  
  **Tags:** machine-learning, python, datasets, intelligent-agent
  
  ---
  
  ## 1 answers
  
  ### Answer by username_1 on 2018-05-16. Score: 1 | Accepted answer
  
  You could formulate the problem of predicting disease severity as a classification one, you give the algorithm those 900 attributes and their corresponding labels (severe/not severe) after training, you give it a new data point with just the 900 attributes, it returns the correct label severe or not.
  
  There is an enormous number of algorithms in the ML literature for classification, some of them formulate the problem explicitly as learnable rules, i.e. let the machine figure out the rules given the attributes.
  
  1. [Classification and Regression Trees by Breiman et al (1984)](ftp://ftp.boulder.ibm.com/software/analytics/spss/support/Stats/Docs/Statistics/Algorithms/14.0/TREE-CART.pdf)
  2. [Random forest by Ho in 1995](https://en.wikipedia.org/wiki/Random_forest)
  3. [XGBoost](https://machinelearningmastery.com/gentle-introduction-xgboost-applied-machine-learning/)
  
  > **Comments:**
  > **username_2**: "it returns the correct label": not always.  
  > **username_1**: yes, I mean in a probabilistic way  
  
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