diff --git a/README.md b/README.md
index dac46e8..045e2a7 100644
--- a/README.md
+++ b/README.md
@@ -25,8 +25,6 @@ llvm_model.compile()
 - Drop-in replacement: The interface of `lleaves.Model` is a subset of `LightGBM.Booster`.
 - Dependencies: `llvmlite` and `numpy`. LLVM comes statically linked.
 
-Some LightGBM features are not yet implemented: multiclass prediction, linear models.
-
 ## Installation
 `conda install -c conda-forge lleaves` or `pip install lleaves` (Linux and MacOS only).
 
diff --git a/docs/development.rst b/docs/development.rst
index 69cb3fe..a03af0d 100644
--- a/docs/development.rst
+++ b/docs/development.rst
@@ -81,6 +81,21 @@ An example from the *model.txt* of the airlines model::
 
 The bitvectors of the first three categorical nodes are <1 x i32>, <1 x i32> and <8 x i32> long.
 
+Multiclass prediction
+*********************
+
+Multiclass prediction works by basically fitting individual forests for each class, and then running a
+softmax across the outputs.
+So for 3 classes with 100 iterations LightGBM will generate 300 trees.
+The trees are saved in the model.txt in strides, like so::
+
+    tree 0 # (=class 0, tree 0)
+    tree 1 # (=class 1, tree 0)
+    tree 2 # (=class 2, tree 0)
+    tree 3 # (=class 0, tree 1)
+    tree 4 # (=class 1, tree 1)
+    ...
+
 Software Architecture Overview
 ------------------------------
 
diff --git a/lleaves/compiler/ast/nodes.py b/lleaves/compiler/ast/nodes.py
index 9f167ec..92f3098 100644
--- a/lleaves/compiler/ast/nodes.py
+++ b/lleaves/compiler/ast/nodes.py
@@ -1,35 +1,35 @@
 from lleaves.compiler.utils import DecisionType
+from dataclasses import dataclass
 
 
-class Forest:
-    def __init__(
-        self,
-        trees: list,
-        features: list,
-        objective_func: str,
-        objective_func_config: str,
-    ):
-        self.trees = trees
-        self.n_args = len(features)
-        self.features = features
-        self.objective_func = objective_func
-        self.objective_func_config = objective_func_config
+class Node:
+    @property
+    def is_leaf(self):
+        return isinstance(self, LeafNode)
 
 
+@dataclass
 class Tree:
-    def __init__(self, idx, root_node, features):
-        self.idx = idx
-        self.root_node = root_node
-        self.features = features
+    idx: int
+    root_node: Node
+    features: list
+    class_id: int
 
     def __str__(self):
         return f"tree_{self.idx}"
 
 
-class Node:
+@dataclass
+class Forest:
+    trees: list[Tree]
+    features: list
+    n_classes: int
+    objective_func: str
+    objective_func_config: str
+
     @property
-    def is_leaf(self):
-        return isinstance(self, LeafNode)
+    def n_args(self):
+        return len(self.features)
 
 
 class DecisionNode(Node):
@@ -74,10 +74,10 @@ def __str__(self):
         return f"node_{self.idx}"
 
 
+@dataclass
 class LeafNode(Node):
-    def __init__(self, idx, value):
-        self.idx = idx
-        self.value = value
+    idx: int
+    value: float
 
     def __str__(self):
         return f"leaf_{self.idx}"
diff --git a/lleaves/compiler/ast/parser.py b/lleaves/compiler/ast/parser.py
index f1244e9..b927ce4 100644
--- a/lleaves/compiler/ast/parser.py
+++ b/lleaves/compiler/ast/parser.py
@@ -1,3 +1,5 @@
+import itertools
+
 from lleaves.compiler.ast.nodes import DecisionNode, Forest, LeafNode, Tree
 from lleaves.compiler.ast.scanner import scan_model_file
 from lleaves.compiler.utils import DecisionType
@@ -18,7 +20,7 @@ def __init__(self, is_categorical):
         self.is_categorical = is_categorical
 
 
-def _parse_tree_to_ast(tree_struct, features):
+def _parse_tree_to_ast(tree_struct, features, class_id):
     n_nodes = len(tree_struct["decision_type"])
     leaves = [
         LeafNode(idx, value) for idx, value in enumerate(tree_struct["leaf_value"])
@@ -78,17 +80,19 @@ def _parse_tree_to_ast(tree_struct, features):
         node.validate()
 
     if nodes:
-        return Tree(tree_struct["Tree"], nodes[0], features)
+        return Tree(tree_struct["Tree"], nodes[0], features, class_id)
     else:
         # special case for when tree is just single leaf
         assert len(leaves) == 1
-        return Tree(tree_struct["Tree"], leaves[0], features)
+        return Tree(tree_struct["Tree"], leaves[0], features, class_id)
 
 
 def parse_to_ast(model_path):
     scanned_model = scan_model_file(model_path)
 
     n_args = scanned_model["general_info"]["max_feature_idx"] + 1
+    n_classes = scanned_model["general_info"]["num_class"]
+    assert n_classes == scanned_model["general_info"]["num_tree_per_iteration"]
     objective = scanned_model["general_info"]["objective"]
     objective_func = objective[0]
     objective_func_config = objective[1] if len(objective) > 1 else None
@@ -99,10 +103,13 @@ def parse_to_ast(model_path):
     assert n_args == len(features), "Ill formed model file"
 
     trees = [
-        _parse_tree_to_ast(tree_struct, features)
-        for tree_struct in scanned_model["trees"]
+        _parse_tree_to_ast(scanned_tree, features, class_id)
+        for scanned_tree, class_id in zip(
+            scanned_model["trees"], itertools.cycle(range(n_classes))
+        )
     ]
-    return Forest(trees, features, objective_func, objective_func_config)
+    assert len(trees) % n_classes == 0, "Ill formed model file"
+    return Forest(trees, features, n_classes, objective_func, objective_func_config)
 
 
 def is_categorical_feature(feature_info: str):
diff --git a/lleaves/compiler/ast/scanner.py b/lleaves/compiler/ast/scanner.py
index 4b20597..445f8ff 100644
--- a/lleaves/compiler/ast/scanner.py
+++ b/lleaves/compiler/ast/scanner.py
@@ -71,6 +71,8 @@ def __init__(self, type: type, is_list=False, null_ok=False):
 
 INPUT_SCAN_KEYS = {
     "max_feature_idx": ScannedValue(int),
+    "num_class": ScannedValue(int),
+    "num_tree_per_iteration": ScannedValue(int),
     "version": ScannedValue(str),
     "feature_infos": ScannedValue(str, True),
     "objective": ScannedValue(str, True),
diff --git a/lleaves/compiler/codegen/codegen.py b/lleaves/compiler/codegen/codegen.py
index 7a5e1ba..0f0906d 100644
--- a/lleaves/compiler/codegen/codegen.py
+++ b/lleaves/compiler/codegen/codegen.py
@@ -1,3 +1,5 @@
+from dataclasses import dataclass
+
 from llvmlite import ir
 
 from lleaves.compiler.utils import ISSUE_ERROR_MSG, MissingType
@@ -28,6 +30,14 @@ def dconst(value):
     return ir.Constant(DOUBLE, value)
 
 
+@dataclass
+class LTree:
+    """Class for the LLVM function of a tree paired with relevant non-LLVM context"""
+
+    llvm_function: ir.Function
+    class_id: int
+
+
 def gen_forest(forest, module):
     """
     Populate the passed IR module with code for the forest.
@@ -81,10 +91,14 @@ def make_tree(tree):
         tree_func.linkage = "private"
         # populate function with IR
         gen_tree(tree, tree_func)
-        return tree_func
+        return LTree(llvm_function=tree_func, class_id=tree.class_id)
 
     tree_funcs = [make_tree(tree) for tree in forest.trees]
 
+    if forest.n_classes > 1:
+        # better locality by running trees for each class together
+        tree_funcs.sort(key=lambda t: t.class_id)
+
     _populate_forest_func(forest, root_func, tree_funcs)
 
 
@@ -189,17 +203,30 @@ def _populate_instruction_block(
         else:
             args.append(el)
     # iterate over each tree, sum up results
-    res = builder.call(tree_funcs[0], args)
-    for func in tree_funcs[1:]:
-        tree_res = builder.call(func, args)
-        res = builder.fadd(tree_res, res)
-    ptr = builder.gep(out_arr, (loop_iter_reg,))
-    res = builder.fadd(res, builder.load(ptr))
+    results = [dconst(0.0) for _ in range(forest.n_classes)]
+    for func in tree_funcs:
+        tree_res = builder.call(func.llvm_function, args)
+        results[func.class_id] = builder.fadd(tree_res, results[func.class_id])
+    res_idx = builder.mul(iconst(forest.n_classes), loop_iter_reg)
+    results_ptr = [
+        builder.gep(out_arr, (builder.add(res_idx, iconst(class_idx)),))
+        for class_idx in range(forest.n_classes)
+    ]
+
+    results = [
+        builder.fadd(result, builder.load(result_ptr))
+        for result, result_ptr in zip(results, results_ptr)
+    ]
     if eval_obj_func:
-        res = _populate_objective_func_block(
-            builder, res, forest.objective_func, forest.objective_func_config
+        results = _populate_objective_func_block(
+            builder,
+            results,
+            forest.objective_func,
+            forest.objective_func_config,
         )
-    builder.store(res, ptr)
+    for result, result_ptr in zip(results, results_ptr):
+        builder.store(result, result_ptr)
+
     tmpp1 = builder.add(loop_iter_reg, iconst(1))
     builder.store(tmpp1, loop_iter)
     builder.branch(condition_block)
@@ -230,7 +257,7 @@ def _populate_forest_func(forest, root_func, tree_funcs):
 
 
 def _populate_objective_func_block(
-    builder, input, objective: str, objective_config: str
+    builder, args, objective: str, objective_config: str
 ):
     """
     Takes the objective function specification and generates the code for it into the builder
@@ -246,23 +273,23 @@ def _populate_sigmoid(alpha):
             raise ValueError(f"Sigmoid parameter needs to be >0, is {alpha}")
 
         # 1 / (1 + exp(- alpha * x))
-        inner = builder.fmul(dconst(-alpha), input)
+        inner = builder.fmul(dconst(-alpha), args[0])
         exp = builder.call(llvm_exp, [inner])
         denom = builder.fadd(dconst(1.0), exp)
         return builder.fdiv(dconst(1.0), denom)
 
     if objective == "binary":
         alpha = objective_config.split(":")[1]
-        return _populate_sigmoid(float(alpha))
+        result = _populate_sigmoid(float(alpha))
     elif objective in ("xentropy", "cross_entropy"):
-        return _populate_sigmoid(1.0)
+        result = _populate_sigmoid(1.0)
     elif objective in ("xentlambda", "cross_entropy_lambda"):
         # naive implementation which will be numerically unstable for small x.
         # should be changed to log1p
-        exp = builder.call(llvm_exp, [input])
-        return builder.call(llvm_log, [builder.fadd(dconst(1.0), exp)])
+        exp = builder.call(llvm_exp, [args[0]])
+        result = builder.call(llvm_log, [builder.fadd(dconst(1.0), exp)])
     elif objective in ("poisson", "gamma", "tweedie"):
-        return builder.call(llvm_exp, [input])
+        result = builder.call(llvm_exp, [args[0]])
     elif objective in (
         "regression",
         "regression_l1",
@@ -272,15 +299,27 @@ def _populate_sigmoid(alpha):
         "mape",
     ):
         if objective_config and "sqrt" in objective_config:
-            return builder.call(llvm_copysign, [builder.fmul(input, input), input])
+            arg = args[0]
+            result = builder.call(llvm_copysign, [builder.fmul(arg, arg), arg])
         else:
-            return input
+            result = args[0]
     elif objective in ("lambdarank", "rank_xendcg", "custom"):
-        return input
+        result = args[0]
+    elif objective == "multiclass":
+        assert len(args)
+        # TODO Check vectorization / vectorize by hand
+        result = [builder.call(llvm_exp, [arg]) for arg in args]
+
+        denominator = dconst(0.0)
+        for r in result:
+            denominator = builder.fadd(r, denominator)
+
+        result = [builder.fdiv(r, denominator) for r in result]
     else:
         raise ValueError(
             f"Objective '{objective}' not yet implemented. {ISSUE_ERROR_MSG}"
         )
+    return result if len(args) > 1 else [result]
 
 
 def _populate_categorical_node_block(
diff --git a/lleaves/data_processing.py b/lleaves/data_processing.py
index 96b12d9..f64874b 100644
--- a/lleaves/data_processing.py
+++ b/lleaves/data_processing.py
@@ -149,20 +149,24 @@ def extract_pandas_traintime_categories(file_path):
     raise ValueError("Ill formatted model file!")
 
 
-def extract_num_feature(file_path):
+def extract_n_features_n_classes(file_path):
     """
-    Extract number of features expected by this model as 'max_feature_idx' + 1
+    Extract number of features and the number of classes of this model
 
     :param file_path: path to model.txt
-    :return: the number of features expected by this model.
+    :return: dict with "n_args": number of features, "n_classes": number of classes
     """
+    res = {}
     with open(file_path, "r") as f:
-        line = f.readline()
-        while line and not line.startswith("max_feature_idx"):
+        for _ in range(2):
             line = f.readline()
-
-        if line.startswith("max_feature_idx"):
-            n_args = int(line.split("=")[1]) + 1
-        else:
-            raise ValueError("Ill formatted model file!")
-    return n_args
+            while line and not line.startswith(("max_feature_idx", "num_class")):
+                line = f.readline()
+
+            if line.startswith("max_feature_idx"):
+                res["n_feature"] = int(line.split("=")[1]) + 1
+            elif line.startswith("num_class"):
+                res["n_class"] = int(line.split("=")[1])
+            else:
+                raise ValueError("Ill formatted model file!")
+    return res
diff --git a/lleaves/lleaves.py b/lleaves/lleaves.py
index 02508d5..5ded840 100644
--- a/lleaves/lleaves.py
+++ b/lleaves/lleaves.py
@@ -10,7 +10,7 @@
 from lleaves import compiler
 from lleaves.data_processing import (
     data_to_ndarray,
-    extract_num_feature,
+    extract_n_features_n_classes,
     extract_pandas_traintime_categories,
     ndarray_to_ptr,
 )
@@ -35,7 +35,9 @@ class Model:
     _execution_engine = None
 
     # number of features (=columns)
-    _num_feature = None
+    _n_feature = None
+    # number of classes
+    _n_classes = None
 
     # prediction function, drops GIL on entry
     _c_entry_func = None
@@ -50,13 +52,23 @@ def __init__(self, model_file):
         self.is_compiled = False
 
         self._pandas_categorical = extract_pandas_traintime_categories(model_file)
-        self._num_feature = extract_num_feature(model_file)
+        num_attrs = extract_n_features_n_classes(model_file)
+        self._n_feature = num_attrs["n_feature"]
+        self._n_classes = num_attrs["n_class"]
 
     def num_feature(self):
         """
         Returns the number of features used by this model.
         """
-        return self._num_feature
+        return self._n_feature
+
+    def num_model_per_iteration(self):
+        """
+        Returns the number of models per iteration.
+
+        This is equal to the number of classes for multiclass models, else will be 1.
+        """
+        return self._n_classes
 
     def compile(self, cache=None):
         """
@@ -95,7 +107,8 @@ def predict(self, data, n_jobs=os.cpu_count()):
             2D float64 numpy arrays have the lowest overhead.
         :param n_jobs: Number of threads to use for prediction. Defaults to number of CPUs. For single-row prediction
             this should be set to 1.
-        :return: 1D numpy array, dtype float64
+        :return: 1D numpy array, dtype float64.
+            If multiclass model: 2D numpy array of shape (n_rows, model.num_model_per_iteration())
         """
         if not self.is_compiled:
             raise RuntimeError(
@@ -112,7 +125,11 @@ def predict(self, data, n_jobs=os.cpu_count()):
 
         # setup input data and predictions array
         ptr_data = ndarray_to_ptr(data)
-        predictions = np.zeros(n_predictions, dtype=np.float64)
+
+        pred_shape = (
+            n_predictions if self._n_classes == 1 else (n_predictions, self._n_classes)
+        )
+        predictions = np.zeros(pred_shape, dtype=np.float64)
         ptr_preds = ndarray_to_ptr(predictions)
 
         if n_jobs == 1:
diff --git a/setup.py b/setup.py
index 178ea39..e380123 100644
--- a/setup.py
+++ b/setup.py
@@ -25,5 +25,5 @@
     long_description=long_description,
     long_description_content_type="text/markdown",
     python_requires=">=3.6",
-    install_requires=["llvmlite>=0.36", "numpy"],
+    install_requires=["llvmlite>=0.36", "numpy", "dataclasses; python_version < '3.7'"],
 )
diff --git a/tests/models/multiclass/model.txt b/tests/models/multiclass/model.txt
new file mode 100644
index 0000000..b5615b6
--- /dev/null
+++ b/tests/models/multiclass/model.txt
@@ -0,0 +1,2422 @@
+tree
+version=v3
+num_class=3
+num_tree_per_iteration=3
+label_index=0
+max_feature_idx=12
+objective=multiclass num_class:3
+feature_names=0 1 2 3 4 5 6 7 8 9 10 11 12
+feature_infos=[-2.4342353470856879:2.1609503228013156] [-1.2983335777173202:2.9745429953195082] [-3.6791622340370145:3.1197718608779978] [-2.6710181364687169:3.1545107139410429] [-2.0882551982316313:4.3713721395547669] [-1.9149662374549228:2.5395154667814048] [-1.5654548527235288:1.7175163293202751] [-1.7876559632056976:2.4024031898537941] [-2.0515133394236567:3.485072512025424] [-1.6342882758947095:2.8947194457156122] [-1.8314920820746869:3.3016942153020343] [-1.8950538941648829:1.9609149917344735] [-1.3849148584226467:2.5479349087557956]
+tree_sizes=544 551 447 556 660 558 658 659 558 659 662 557 660 659 559 655 661 657 659 657 657 659 662 658 662 659 656 660 657 657 663 664 658 663 658 657 660 658 657 658 663 656 665 663 655 660 657 660 662 663 657 657 662 657 663 658 662 658 764 657 662 766 658 663 658 662 659 762 657 659 767 660 663 659 661 553 660 660 555 762 663 665 761 655 555 656 657 665 764 661 659 762 656 658 658 662 664 766 658 660 663 666 662 658 656 663 663 665 658 767 662 767 763 664 660 766 663 659 769 764
+
+Tree=0
+num_leaves=4
+num_cat=0
+split_feature=12 6 0
+split_gain=59.8697 2.92663 3.55271e-15
+threshold=0.40795511641254606 0.15132824247368962 -0.69868807122782772
+decision_type=2 2 2
+left_child=1 2 -1
+right_child=-2 -3 -4
+leaf_value=-1.160925920495363 -1.076627673852488 -1.1380449678351541 -1.160925920495363
+leaf_weight=8.5223724246025103 12.455775082111357 6.5556710958480826 13.111342191696169
+leaf_count=26 38 20 40
+internal_value=-1.1314 -1.1556 -1.16093
+internal_weight=0 28.1894 21.6337
+internal_count=124 86 66
+is_linear=0
+shrinkage=1
+
+
+Tree=1
+num_leaves=4
+num_cat=0
+split_feature=9 9 0
+split_gain=61.792 2.26404 0.721063
+threshold=-0.53556051917167058 -0.090013436720989409 -0.85309619185575125
+decision_type=2 2 2
+left_child=2 -2 -1
+right_child=1 -3 -4
+leaf_value=-0.87784902076425253 -0.94251000009467512 -0.96152793519185831 -0.8904008579283933
+leaf_weight=9.6798581779003161 7.887291848659518 19.718229621648788 7.1702653169631949
+leaf_count=27 22 55 20
+internal_value=-0.928461 -0.956094 -0.88319
+internal_weight=0 27.6055 16.8501
+internal_count=124 77 47
+is_linear=0
+shrinkage=1
+
+
+Tree=2
+num_leaves=3
+num_cat=0
+split_feature=11 8
+split_gain=61.9495 0.505091
+threshold=-0.68035307296581804 -0.31695005052030539
+decision_type=2 2
+left_child=-1 -2
+right_child=1 -3
+leaf_value=-1.2070661441972923 -1.2829264408864776 -1.2927986712775361
+leaf_weight=11.547542154788969 6.0776537656784084 20.056257426738739
+leaf_count=38 20 66
+internal_value=-1.26493 -1.2905
+internal_weight=0 26.1339
+internal_count=124 86
+is_linear=0
+shrinkage=1
+
+
+Tree=3
+num_leaves=4
+num_cat=0
+split_feature=12 6 9
+split_gain=55.7113 2.85964 0.000161186
+threshold=0.40795511641254606 0.15132824247368962 -0.2197844316095374
+decision_type=2 2 2
+left_child=1 2 -1
+right_child=-2 -3 -4
+leaf_value=-0.029054555287911489 0.051840344709510128 -0.0064406419777528227 -0.029219644756495307
+leaf_weight=10.939959913492205 12.784547895193098 6.5283163189887992 10.366198152303697
+leaf_count=34 38 20 32
+internal_value=0 -0.0238122 -0.0291349
+internal_weight=0 27.8345 21.3062
+internal_count=124 86 66
+is_linear=0
+shrinkage=0.03
+
+
+Tree=4
+num_leaves=5
+num_cat=0
+split_feature=9 9 12 12
+split_gain=58.1961 2.18516 0.687031 7.6376e-05
+threshold=-0.53556051917167058 -0.090013436720989409 -0.59197591750974599 0.40795511641254606
+decision_type=2 2 2 2
+left_child=2 -2 -1 -3
+right_child=1 3 -4 -5
+leaf_value=0.048686944957037501 -0.013735965837632463 -0.032528136958421092 0.036506394068981436 -0.032408937432166711
+leaf_weight=9.8023761808872241 7.8556687831878689 10.656040698289873 7.2500125467777243 8.8604672551155073
+leaf_count=27 22 30 20 25
+internal_value=0 -0.0270963 0.0435082 -0.032474
+internal_weight=0 27.3722 17.0524 19.5165
+internal_count=124 77 47 55
+is_linear=0
+shrinkage=0.03
+
+
+Tree=5
+num_leaves=4
+num_cat=0
+split_feature=11 11 9
+split_gain=57.3497 0.509548 4.27576e-05
+threshold=-0.68035307296581804 0.18829925847304788 -0.32143837760556654
+decision_type=2 2 2
+left_child=-1 -2 -3
+right_child=1 2 -4
+leaf_value=0.054290229991174742 -0.017581036621012472 -0.027532359386351249 -0.027620852786142396
+leaf_weight=11.951980292797087 5.9862624704837826 10.432896345853807 9.2893663942813856
+leaf_count=38 20 35 31
+internal_value=0 -0.0252472 -0.027574
+internal_weight=0 25.7085 19.7223
+internal_count=124 86 66
+is_linear=0
+shrinkage=0.03
+
+
+Tree=6
+num_leaves=5
+num_cat=0
+split_feature=12 0 4 6
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+
+
+Tree=102
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+split_gain=11.1713 1.16156 0.187943 0.00731732
+threshold=1.0000000180025095e-35 0.45628467106904042 1.1163802839358452 -0.57881751746785304
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+
+
+Tree=103
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+threshold=-0.67830861354907324 -0.57866837472720112 -0.094189304596502321 0.34684603603737024
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+
+
+Tree=104
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+split_feature=6 11 9 12
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+threshold=-0.4560716245405308 -1.2382679850281793 -0.57881751746785304 0.89359041154996521
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+internal_weight=0 13.4786 11.05 6.89575
+internal_count=124 44 80 45
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+
+
+Tree=105
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+split_feature=6 0 9 4
+split_gain=10.7838 2.84711 0.00466701 0.000960401
+threshold=0.15132824247368962 0.16599740428854384 -0.57881751746785304 -0.22760167158663605
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+internal_weight=0 14.3496 10.6812 7.27329
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+
+
+Tree=106
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+split_gain=11.9076 0.925303 0.55741 0.00361232
+threshold=-0.49446637079029704 -0.090013436720989409 -0.43593572431805083 0.27261413252178995
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+
+
+Tree=107
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+threshold=-0.88275748153399147 -0.49335377378417428 0.23067254293348008 -0.69985214462294476
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+internal_count=124 91 78 57
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+
+
+Tree=108
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+split_gain=10.3676 2.50372 0.00434364 0.00101594
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+
+
+Tree=109
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+split_feature=9 12 10 12 9
+split_gain=11.4937 1.98378 0.612049 0.0203663 0.0154504
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+internal_weight=0 17.8566 13.2657 9.87459 9.00745
+internal_count=124 84 66 40 47
+is_linear=0
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+
+
+Tree=110
+num_leaves=5
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+split_feature=10 6 11 12
+split_gain=10.6967 2.51496 0.00810299 0.00108125
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+
+Tree=111
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+split_gain=9.9704 1.0075 0.191914 0.00797992 0.000691093
+threshold=1.0000000180025095e-35 0.45628467106904042 1.1163802839358452 0.60348090514247621 -0.67830861354907324
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+internal_weight=0 12.542 11.4165 8.48265 5.68786
+internal_count=124 48 76 58 42
+is_linear=0
+shrinkage=0.03
+
+
+Tree=112
+num_leaves=6
+num_cat=0
+split_feature=9 4 10 9 4
+split_gain=11.0357 2.46912 0.611173 0.016679 0.0119965
+threshold=-0.67830861354907324 -0.71909505598342716 0.60348090514247621 0.089503106208168551 -0.29781501221474904
+decision_type=2 2 2 2 2
+left_child=4 -2 3 -3 -1
+right_child=1 2 -4 -5 -6
+leaf_value=0.02517660593580787 0.0068317990186698143 -0.025402072575950223 -0.010133973767010959 -0.022881704729514261 0.027296118273542631
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+internal_weight=0 17.538 13.5052 9.47446 9.66155
+internal_count=124 84 69 51 40
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+
+
+Tree=113
+num_leaves=5
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+split_feature=6 10 11 7
+split_gain=10.249 1.53782 0.00757239 0.00115964
+threshold=-0.88275748153399147 -0.49335377378417428 0.23067254293348008 -0.69985214462294476
+decision_type=2 2 2 2
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+internal_weight=0 13.2421 10.2674 7.01511
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+
+
+Tree=114
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+num_cat=0
+split_feature=6 0 9 1
+split_gain=9.62805 2.3031 0.00395175 0.000949237
+threshold=0.15132824247368962 0.22776065253971303 -0.57881751746785304 0.66306039540987938
+decision_type=2 2 2 2
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+internal_weight=0 13.5638 9.88065 6.78822
+internal_count=124 57 67 41
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+
+
+Tree=115
+num_leaves=6
+num_cat=0
+split_feature=9 4 10 9 4
+split_gain=10.6087 2.38831 0.6051 0.0185287 0.011536
+threshold=-0.67830861354907324 -0.71909505598342716 0.60348090514247621 1.0000000180025095e-35 -0.29781501221474904
+decision_type=2 2 2 2 2
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+right_child=1 2 -4 -5 -6
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+internal_value=0 -0.0135874 -0.0197401 -0.0239547 0.0259883
+internal_weight=0 17.2209 13.2134 9.23385 9.43643
+internal_count=124 84 69 51 40
+is_linear=0
+shrinkage=0.03
+
+
+Tree=116
+num_leaves=5
+num_cat=0
+split_feature=10 6 11 0
+split_gain=9.91196 2.33512 0.00959488 0.00103129
+threshold=-0.75659409672656996 -0.76730130846517275 0.18829925847304788 0.61069279169696389
+decision_type=2 2 2 2
+left_child=-1 -2 -3 -4
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+internal_value=0 -0.0161119 -0.0227772 -0.0222285
+internal_weight=0 13.6571 10.5976 7.7384
+internal_count=124 92 82 64
+is_linear=0
+shrinkage=0.03
+
+
+Tree=117
+num_leaves=5
+num_cat=0
+split_feature=6 12 11 12
+split_gain=9.24118 1.35477 0.233354 0.00463328
+threshold=0.32200258527107412 0.76621066200572407 0.18829925847304788 -0.2926335060807797
+decision_type=2 2 2 2
+left_child=2 -2 3 -1
+right_child=1 -3 -4 -5
+leaf_value=-0.022629506305326223 0.0058239025143824964 0.026144486901705684 -0.01303791990612963 -0.024124465277666592
+leaf_weight=5.1641987562179557 5.1173630803823453 6.9808921068906784 2.7390089854598036 2.9213012158870724
+leaf_count=37 22 29 20 16
+internal_value=0 0.0175492 -0.0206059 -0.0231696
+internal_weight=0 12.0983 10.8245 8.0855
+internal_count=124 51 73 53
+is_linear=0
+shrinkage=0.03
+
+
+Tree=118
+num_leaves=6
+num_cat=0
+split_feature=9 4 10 9 4
+split_gain=10.1936 2.31533 0.598355 0.0172433 0.0101477
+threshold=-0.67830861354907324 -0.71909505598342716 0.60348090514247621 1.0000000180025095e-35 -0.29781501221474904
+decision_type=2 2 2 2 2
+left_child=4 -2 3 -3 -1
+right_child=1 2 -4 -5 -6
+leaf_value=0.024877871393677732 0.0066045862490326001 -0.025351041352551621 -0.0097944554863674572 -0.022694216198568654 0.026871812829434676
+leaf_weight=4.9167407900094986 3.9828602075576809 3.8310579061508161 3.9309362769126883 5.1594076603651047 4.3114450275897997
+leaf_count=23 15 17 18 34 17
+internal_value=0 -0.0133934 -0.0195576 -0.0238264 0.0258094
+internal_weight=0 16.9043 12.9214 8.99047 9.22819
+internal_count=124 84 69 51 40
+is_linear=0
+shrinkage=0.03
+
+
+Tree=119
+num_leaves=6
+num_cat=0
+split_feature=10 6 11 9 12
+split_gain=9.49304 2.68686 0.016789 0.00102233 0.00244551
+threshold=-0.75659409672656996 -0.88275748153399147 0.13180154585913784 -0.67830861354907324 0.93339658328254049
+decision_type=2 2 2 2 2
+left_child=-1 -2 -3 -4 -5
+right_child=1 2 3 4 -6
+leaf_value=0.024136468739497662 0.010521117753537009 -0.024842840035816598 -0.021655184535634777 -0.023037885380915259 -0.021730912635456453
+leaf_weight=8.7994236573576945 2.7308668792247763 2.801385283470152 2.6416209414601317 2.5636880323290834 2.5903929173946381
+leaf_count=32 9 16 26 17 24
+internal_value=0 -0.016013 -0.0228509 -0.0221351 -0.022381
+internal_weight=0 13.328 10.5971 7.7957 5.15408
+internal_count=124 92 83 67 41
+is_linear=0
+shrinkage=0.03
+
+
+end of trees
+
+feature_importances:
+9=148
+12=107
+6=63
+4=41
+11=38
+0=36
+10=24
+5=8
+3=6
+1=4
+7=4
+8=3
+2=1
+
+parameters:
+[boosting: gbdt]
+[objective: multiclass]
+[metric: multi_logloss]
+[tree_learner: serial]
+[device_type: cpu]
+[linear_tree: 0]
+[data: ]
+[valid: ]
+[num_iterations: 40]
+[learning_rate: 0.03]
+[num_leaves: 31]
+[num_threads: 0]
+[deterministic: 0]
+[force_col_wise: 0]
+[force_row_wise: 0]
+[histogram_pool_size: -1]
+[max_depth: 10]
+[min_data_in_leaf: 20]
+[min_sum_hessian_in_leaf: 0.001]
+[bagging_fraction: 1]
+[pos_bagging_fraction: 1]
+[neg_bagging_fraction: 1]
+[bagging_freq: 0]
+[bagging_seed: 3]
+[feature_fraction: 1]
+[feature_fraction_bynode: 1]
+[feature_fraction_seed: 2]
+[extra_trees: 0]
+[extra_seed: 6]
+[early_stopping_round: 0]
+[first_metric_only: 0]
+[max_delta_step: 0]
+[lambda_l1: 0]
+[lambda_l2: 0]
+[linear_lambda: 0]
+[min_gain_to_split: 0]
+[drop_rate: 0.1]
+[max_drop: 50]
+[skip_drop: 0.5]
+[xgboost_dart_mode: 0]
+[uniform_drop: 0]
+[drop_seed: 4]
+[top_rate: 0.2]
+[other_rate: 0.1]
+[min_data_per_group: 100]
+[max_cat_threshold: 32]
+[cat_l2: 10]
+[cat_smooth: 10]
+[max_cat_to_onehot: 4]
+[top_k: 20]
+[monotone_constraints: ]
+[monotone_constraints_method: basic]
+[monotone_penalty: 0]
+[feature_contri: ]
+[forcedsplits_filename: ]
+[refit_decay_rate: 0.9]
+[cegb_tradeoff: 1]
+[cegb_penalty_split: 0]
+[cegb_penalty_feature_lazy: ]
+[cegb_penalty_feature_coupled: ]
+[path_smooth: 0]
+[interaction_constraints: ]
+[verbosity: 1]
+[saved_feature_importance_type: 0]
+[max_bin: 255]
+[max_bin_by_feature: ]
+[min_data_in_bin: 3]
+[bin_construct_sample_cnt: 200000]
+[data_random_seed: 1]
+[is_enable_sparse: 1]
+[enable_bundle: 1]
+[use_missing: 1]
+[zero_as_missing: 0]
+[feature_pre_filter: 1]
+[pre_partition: 0]
+[two_round: 0]
+[header: 0]
+[label_column: ]
+[weight_column: ]
+[group_column: ]
+[ignore_column: ]
+[categorical_feature: ]
+[forcedbins_filename: ]
+[objective_seed: 5]
+[num_class: 3]
+[is_unbalance: 0]
+[scale_pos_weight: 1]
+[sigmoid: 1]
+[boost_from_average: 1]
+[reg_sqrt: 0]
+[alpha: 0.9]
+[fair_c: 1]
+[poisson_max_delta_step: 0.7]
+[tweedie_variance_power: 1.5]
+[lambdarank_truncation_level: 30]
+[lambdarank_norm: 1]
+[label_gain: ]
+[eval_at: ]
+[multi_error_top_k: 1]
+[auc_mu_weights: ]
+[num_machines: 1]
+[local_listen_port: 12400]
+[time_out: 120]
+[machine_list_filename: ]
+[machines: ]
+[gpu_platform_id: -1]
+[gpu_device_id: -1]
+[gpu_use_dp: 0]
+[num_gpu: 1]
+
+end of parameters
+
+pandas_categorical:[]
diff --git a/tests/test_dataprocessing.py b/tests/test_dataprocessing.py
index 7502cc1..964c444 100644
--- a/tests/test_dataprocessing.py
+++ b/tests/test_dataprocessing.py
@@ -6,7 +6,7 @@
 
 from lleaves.data_processing import (
     data_to_ndarray,
-    extract_num_feature,
+    extract_n_features_n_classes,
     extract_pandas_traintime_categories,
     ndarray_to_ptr,
 )
@@ -57,9 +57,11 @@ def test_n_args_extract(tmp_path):
             (line for line in lines if not line.startswith("max_feature_idx"))
         )
 
-    assert extract_num_feature(model_file) == 5
+    res = extract_n_features_n_classes(model_file)
+    assert res["n_class"] == 1
+    assert res["n_feature"] == 5
     with pytest.raises(ValueError):
-        extract_num_feature(mod_model_file)
+        extract_n_features_n_classes(mod_model_file)
 
 
 def test_no_data_modification():
diff --git a/tests/test_tree_output.py b/tests/test_tree_output.py
index 680927c..467d163 100644
--- a/tests/test_tree_output.py
+++ b/tests/test_tree_output.py
@@ -11,6 +11,7 @@
     "tests/models/NYC_taxi/",
     "tests/models/single_tree/",
     "tests/models/tiniest_single_tree/",
+    "tests/models/multiclass/",
 ]
 
 
@@ -55,6 +56,9 @@ def llvm_lgbm_model_cat(request):
 def test_attribute_similarity(llvm_lgbm_model):
     llvm_model, lightgbm_model = llvm_lgbm_model
     assert llvm_model.num_feature() == lightgbm_model.num_feature()
+    assert (
+        llvm_model.num_model_per_iteration() == lightgbm_model.num_model_per_iteration()
+    )
 
 
 @settings(deadline=1000)