EP 4. MACHINE LEARNING CYCLE
EP 4. MACHINE LEARNING CYCLE: Creating a machine learning application or implementing a machine learning algorithm to some data is an iterative process . We can't simply develop a machine learning algorithm and just leave it because data changes , preferences emerge. When we train our machine learning model with a machine learning algorithm, for each and every algorithm there is different accuracy. So we should choose which one fits correctly in terms of all metrics like f-square, mean absolute error, p-value etc. The machine learning cycle is continous cycle and choosing which algorithm fits is only one of the step in the cycle.The steps in the machine learning cycle are as follows: 1> Identify the data : First of all when we have a dataset full of data and features(columns) we have to select which feature we want because it will be an underfit when we try to predict. We can do feature engineering when we want to add any features.Then the next step is to observe how the