For large data yes. For small data sets feature engineering is often more important than the machine learning model used. (Kaggle contests are frequently are wo

For large data yes. For small data sets feature engineering is often more important than the machine learning model used. (Kaggle contests are frequently are won by Random Forrests + Feature Engineering) Another important thing to consider is that a lot of the actual work in setting up a machine learning pipeline has nothing to do with machine learning (making sure that data is clean, exceptions are handled, etc). On a small data set an SVM can achieve good performance out of the box. Starting with an SVM can be a really helpful in validating the basic approach and understand the nature of the problem before you sink in a bunch to time/money collecting data and training a complicated model.