# Using the appropriate machine learning algorithm

I am not sure if this is the right forum to ask this.

I have some data of the houses, like their size(in square meters), if they use aircondition, how many residents live in, I have their electricity consumption as well. I want to train any Machine Learning Algorithm to the dataset above, in order to create a model that estimates the houses consumption.

I tried many different algorithms (using weka tool), but I did not have good results. I was said that SVMs could solve this problem, with the right preprocessing. However, i did not have good results either.

Can anyone help me, in the way i should approach this problem?

• How is your error $90\%$? Are you counting it as a total miss if you don't get the exact value? Usually, when trying to predict a continuous value, people use a cost function like the squared error. It might make sense to rescale the output, but be careful that when you don't have a lot of data, you want to use a simple model. Trying a lot of ways to rescale the output is equivalent to using a more complicated model which may tend to overfit on a small sample. – Douglas Zare Mar 11 '13 at 21:31
• I wouldn't call that a $90\%$ error. That sounds like linear regression explains up to $50\%$ of the variance. Depending on the information you have, you might not be able to do much better. – Douglas Zare Mar 12 '13 at 14:47