Bad data

After explaining to your dear friend from China (henceforth referred to as Chan) the miscalculation that just occurred, you instruct him to set up sensors and start collecting local air pressure and temperature to construct a labeled dataset of sunny and rainy days, just like you did in Hawaii. Chan diligently places sensors on his roof and in his fields. Unfortunately, Chan's roof is made of a reinforced metal alloy with a high thermal conductivity, which erratically inflates the reading from both the pressure and temperature sensors from the roof in an inconsistent and unreliable manner. This corrupted data, when fed to our predictive model, will naturally produce suboptimal results, as the learned line is perturbed by noisy and misrepresentative data. A clear remedy would be to replace the sensors, or simply discard the faulty sensor readings.