diff --git a/celltypist/train.py b/celltypist/train.py
index f0bdcda..fc6d128 100644
--- a/celltypist/train.py
+++ b/celltypist/train.py
@@ -165,7 +165,7 @@ def train(X = None,
           with_mean: bool = True,
           check_expression: bool = True,
           #LR param
-          C: float = 1.0, solver: Optional[str] = None, max_iter: int = 1000, n_jobs: Optional[int] = None,
+          C: float = 1.0, solver: Optional[str] = None, max_iter: Optional[int] = None, n_jobs: Optional[int] = None,
           #SGD param
           use_SGD: bool = False, alpha: float = 0.0001,
           #mini-batch
@@ -218,7 +218,7 @@ def train(X = None,
         Maximum number of iterations before reaching the minimum of the cost function.
         Try to decrease `max_iter` if the cost function does not converge for a long time.
         This argument is for both traditional and SGD logistic classifiers, and will be ignored if mini-batch SGD training is conducted (`use_SGD = True` and `mini_batch = True`).
-        (Default: 1000)
+        Default to 200, 500, and 1000 for large (>500k cells), medium (50-500k), and small (<50k) datasets, respectively.
     n_jobs
         Number of CPUs used. Default to one CPU. `-1` means all CPUs are used.
         This argument is for both traditional and SGD logistic classifiers.
@@ -314,6 +314,14 @@ def train(X = None,
     #sklearn (Cython) does not support very large sparse matrices for the time being
     if isinstance(indata, spmatrix) and ((indata.indices.dtype == 'int64') or (indata.indptr.dtype == 'int64')):
         indata = indata.toarray()
+    #max_iter
+    if max_iter is None:
+        if indata.shape[0] < 50000:
+            max_iter = 1000
+        elif indata.shape[0] < 500000:
+            max_iter = 500
+        else:
+            max_iter = 200
     #classifier
     if use_SGD or feature_selection:
         classifier = _SGDClassifier(indata = indata, labels = labels, alpha = alpha, max_iter = max_iter, n_jobs = n_jobs, mini_batch = mini_batch, batch_number = batch_number, batch_size = batch_size, epochs = epochs, balance_cell_type = balance_cell_type, **kwargs)