Hey, Very nice video and easy to understand. My Question: Is there a function that would give me the Mean Average Error of the function regarding to my Dataset?
Thank you so much for posting this. Ques: The curve_fit function returns the covariances between the parameters (coefficients for x and the intercept, etc) If there is a high covariance between the parameters what does this tell us? Is this bad? If so why? thanx!
Is there a method to fit my data ponts with the curve of best fit? That is I do not want to define my function, but let the method handle it? I just want the equation of the best fitting curve.
he took a slight shortcut here. Basically, you approximate the trend-line shape (here, logarithmic), plug in the appropriate formula, then let SciPy do the rest. The formula chosen is the best guess for this graph shape (a * log(x) + b), where (crudely) 'a' will affect the steepness of the curve and 'b' will affect the y axis offset. The a and b values are left entirely up to SciPy to calculate.
This is a fine video. Maybe not only one you'd like to watch for curve_fit but it shows you how to think of linear fit
Thank you so much! You don't know how much you had helped me there
referring to: 4:00. What is at the other indices of "constants"? For example, what does "constants[1]" and "constants[2]" return?
Hey, Very nice video and easy to understand. My Question: Is there a function that would give me the Mean Average Error of the function regarding to my Dataset?
Thank you so much for posting this. Ques: The curve_fit function returns the covariances between the parameters (coefficients for x and the intercept, etc) If there is a high covariance between the parameters what does this tell us? Is this bad? If so why? thanx!
you made me happy, Thank you
Thank you very much .a good understanding
Thank you!
too good bro
safed my life
Is there a method to fit my data ponts with the curve of best fit? That is I do not want to define my function, but let the method handle it? I just want the equation of the best fitting curve.
6:10 so how can you get the formula of that trend-line? The a and b are obviously not the ones in ax+b, so how do I get this formula?
he took a slight shortcut here. Basically, you approximate the trend-line shape (here, logarithmic), plug in the appropriate formula, then let SciPy do the rest. The formula chosen is the best guess for this graph shape (a * log(x) + b), where (crudely) 'a' will affect the steepness of the curve and 'b' will affect the y axis offset. The a and b values are left entirely up to SciPy to calculate.