->
Consider this learning curve: ->
Does this plot indicate high bias, high variance, or both? ->
The blue training error curve is relatively low, and the red dev error curve is much higher than the blue training error. Thus, the bias is small, but the variance is large. Adding more training data will probably help close the gap between dev error and training error. ->
Now, consider this: ->
This time, the training error is large, as it is much higher than the desired level of performance. The dev error is also much larger than the training error. Thus, you have significant bias and significant variance. You will have to find a way to reduce both bias and variance in your algorithm. ->