Why deep learning models underperforms than the machine learning model

Question

I have read that deep learning models outperforms than the traditional machine learning models.

I have a time-series classification problem where the output is 0 or 1. I used LSTM to classify my timeseries as follows.

model = Sequential()
model.add(Conv1D(10, kernel_size=3, input_shape=(25,4)))
model.add(Conv1D(10, kernel_size=2))
model.add(GlobalMaxPooling1D())
model.add(Dense(10))
model.add(Dense(1, activation='sigmoid'))
model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])

Unfortunately, my deep learning model gives very bad results (e.g., 0.333333). I am worried why this happens. Then I tried a machine learning model (using randomforest) and it gave accuracy about 0.6.

I am upsetting why the deep learning model gives that bad results. I would like to get your feedback on why this happens, and is there a way to avoid this.

I am happy to provide more details if needed.

Answer 1

it's not true that deep learning models outperforms the traditional machine learning or even statistical models. It all depends on the problem, what kind of data you have, how many, what's the dimensional etc.

In general, DL might outperform other models in situations where you have a lot of data (10k, 100k, 1m) and when the model is very nonlinear such as in computer vision applications. In other settings DL models are not necessarily as great.

For example in time series forecasting competition, Makridakis et al. https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0194889 the best machine learning model including LSTM was worse than the worst statistical model.