Machine_Learning

 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...

 when we are thinking about the evaluation of classification machine learning problem, confusion Metrix comes in top. as the name describes confusion, really very confusing to understand this scenario. but it is not too hard to understand if you apply some logic behind this concept. so generally, this concept is used to understand how much record we have correctly predicted and incorrectly. in classification problem we have values in categories might be 2 or multiple  categories. binary classification Metrix. we have 0 for negative and 1 for positive in targeted variable. and let's consider we have total 50 target record and out of 50 we have 30 positive and 20 negative, please keep in mind. now, consider we have a machine learning model and result for output is 35 positive and 15 negative. but we are unable to decide which one predicted correctly and which one in incorrect.  so, let's understand some concept in confusion metix. 0--negative 1--positive. TP is called true ...

 Lets understand the concept of bias and variance. how we can define bias and variance is a major concern. lets understand with an example we have one datasets with x(independent variable),y(dependent) and y'(predicted) variable. the difference between y and y' is called bias, so the actual value is 5 and we get 6 in predicted called bias. bias is the error between the average model and the ground truth model. the bias is the estimated function of the that tell the capacity of the model to predict the value. high bias means: the model is underfitting i.e-high error on both training and test data and model is simple. variance is the average variability in the model prediction for the given datasets. high variance means: model is complex and over fitting model i.e less error on training data and high error on test data. so in this case need to work on noisy input. upper both picture share the bais and lower is variance. left side has low and right side has high.  this picture de...