Top Challenges of Supervised Learning: Overcoming Obstacles in Machine Learning

Personnel limitations: Supervised learning models can require certain levels of expertise to structure accurately. 

Human involvement: Supervised learning models are incapable of self-learning. Data scientists must validate the models’ performance output. 

Time requirements: Training datasets are large and must be manually labeled, which makes the supervised learning process time-intensive. 

Inflexibility: Supervised learning models struggle to label data outside the bounds of their training datasets. An unsupervised learning model might be more capable of dealing with new data. 

Bias: Datasets risk a higher likelihood of human error and bias, resulting in algorithms learning incorrectly. 

Overfitting: Supervised learning can sometimes result in overfitting: where a model becomes too closely tailored to its training dataset. High accuracy in training can indicate overfitting as opposed to generally strong performance. Avoiding overfitting requires that models be tested with data that is different from the training data.