we all know machine learning model do not understand text data, so we need to transform the text data to number. i.e we need to transform the raw data to well prepared data to implement the machine learning model for prediction. the process to convert the raw data to make well prepared data is called feature engineering.
Welcome to this post, you fall in very good place and interesting part of machine learning implementation. Evaluation- how we feel with this word, definitely we are going to examine something, i .e out machine learning model. feels very responsible state of mind- going to check and correcting our model. when we are discussing about the machine learning, it divided into 3 parts supervised, unsupervised and reinforcement. we are not going to discuss in detail about this you can use other Resorces to learn or our other post. supervised machine learning is of two types regression and classification. we will learn to evaluate these two separately in this post. Evaluation metrics for Regression: when we have made the regression model where our target value is continuous, we use following method to evaluate the model: MAE: (mean absolute error) MSE: (mean square error) RMSE: (root mean square error) R squared error Adjusted R squared. before we discussed about these techniques for evalua...
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