I'm a newcomer in Machine learning (also some statistics), been learning knowledge (supervised/unsupervised learning algorithms, relevant optimization methods, regularizations, some philosophies (such as bias-variance trade-off?)) for a while. I know that without any real practice, I would not gain deep understanding of those machine learning stuff.
So I begin with some classification problem with real data, say handwritten digit classification (MNIST). To my surprise, without any feature learning/engineering, the accuracy reaches 0.97 using random-forest classifier with raw pixel values as input. I also tried other learning algorithms, such as SVM, LR with parameters being tuned.
Then I got lost, would it be too easy or am I missing anything here? Just pick up a learning algorithm from the toolkit and tune some parameters?
If that would be all about machine learning in practice, then I would be losing my interest in this field. I thought and read some blogs for a few days, and I came to some conclusions:
The most important part of machine learning in practice is feature engineering, that is, given the data, find out better representation of features.
Which learning algorithm to use is also important, also the parameter tuning, but the final choice is more about experimentation.
I'm not sure I understand it correctly, hoping anyone can correct me and give me some suggestion about machine learning in practice.