intro

• here by increasing bias, the training data will give nice result, but test data will not give proper result,
• and as we increase bias, varience will also increase as the predicting point will be far away from the curve, will will give increase in output difference of the model: eg: model ko 20 predict karna tha, par usne predict kar diya 40, thus varience increase

yaha, jo apne training data ko hi nahi samaj pa raha vo hai high biased: ye last wala apne train data ko exceptionally well samaj pa raha hai, thus low bias

training and testing ke error ke bich ka jo difference hai, vo hai variance

• in left, since inke model rough hai, to inke error ka difference almost same hoga
• par last wale mai training ka zero hai, aur test ka kaafi jyada hai, to varience kaafi badh jaye ga