Deploying a machine learning model typically takes the following five steps:
1. Data collection.
2. Data preprocessing:
1) Data cleaning;
2) Data transformation;
3) Divide data into training and testing sets.
3. Build a model on training data.
4. Evaluate the model on the test data.
5. If the performance is satisfying, deploy to the real system.
This process can be iterative, meaning we can re-start from step 1 again. For example, after a model is deployed, we can collect new data and repeat this process. Let’s look at the details of each step:
1. Data Collection:
At this stage, we want to collect all relevant data. For an online business, user click, search queries, and browsing information should be all be captured and saved into the database.
In manufacturing, log data capture machine status and activities. Such data are used to produce maintenance schedules and predict required parts for replacement.
2. Data Preprocessing:
The data used in Machine Learning describes factors, attributes, or features of an observation. Simple first steps in looking at the data include finding missing values. What is the significance of that missing value? Would replacing a missing data value with the median value for the feature be acceptable? For example, perhaps the person filling out a questionnaire doesn't want to reveal his salary. This could be because the person has a very low salary or a very high salary. In this case, perhaps using other features to predict the missing salary data might be appropriate. One might infer the salary from the person’s zip code. The fact that the value is missing may be important. There are machine learning methods that ignore missing values and one of these could be used for this data set.
2) Data Transformation:
In general we work with both numerical and categorical data. Numerical data consists of actual numbers, while categorical data have a few discrete values. Examples of categorical data include eye color, species type, marriage status, or gender. Actually a zip code is categorical. The zip code is a number but there is no meaning to adding two zip codes. There may or may not be an order to categorical data. For instance good, better, best is descriptive categorical data which has an order.
3) After the data has been cleaned and transformed it needs to be split into a training set and a test set.
3. Model Building:
This training data set is used to create the model which is used to predict the answers for new cases in which the answer or target is unknown. For example, Section 1.3 describes how a decision tree is built using the training data set. Several different modeling techniques have been introduced and will be discussed in detail in future sections. Various models can be built using the same training data set.
4. Model Evaluation
Once the model is built with the training data, it is used to predict the targets for the test data. First the target values are removed from the test data set. The model is applied to the test data set to predict the target values for the test data. The predicted value of the target is then compared with the actual target value. The accuracy of the model is the percentage of correct predictions made. These accuracies of can be used to compare the different models. Several other ways to compare model accuracy are discussed in the next section on Performance Evaluation.
5. Model Deployment:
This is the most important step. If the speed and accuracy of the model is acceptable, then that model should be deployed in the real system. The model that is used in production should be made with all the available data. Models improve with the amount of available data used to create the model. The results of the model need to be incorporated in the business strategy. Data mining models provide valuable information which give companies great advantages. Obama won the election in part by incorporating the data mining results into his campaign strategy. The last chapter of this book provides information in how a company can incorporate data mining results into its daily business.