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added random forest classifier

master
mahehsma 2024-06-07 09:36:01 +02:00
parent 45195943d7
commit 77c9299308
1 changed files with 160 additions and 99 deletions
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Experiments.ipynb
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@ -2,7 +2,7 @@
"cells": [ "cells": [
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 2, "execution_count": 4,
"id": "initial_id", "id": "initial_id",
"metadata": { "metadata": {
"jupyter": { "jupyter": {
@ -12,12 +12,14 @@
"outputs": [], "outputs": [],
"source": [ "source": [
"import pandas as pd\n", "import pandas as pd\n",
"from sklearn.preprocessing import MinMaxScaler, StandardScaler" "from sklearn.preprocessing import MinMaxScaler, StandardScaler\n",
"from sklearn.model_selection import KFold\n",
"from sklearn import decomposition"
] ]
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 21, "execution_count": 2,
"id": "67503952-9074-4cdb-9d7e-d9142f7c319c", "id": "67503952-9074-4cdb-9d7e-d9142f7c319c",
"metadata": {}, "metadata": {},
"outputs": [ "outputs": [
@ -216,7 +218,7 @@
"[5 rows x 28 columns]" "[5 rows x 28 columns]"
] ]
}, },
"execution_count": 21, "execution_count": 2,
"metadata": {}, "metadata": {},
"output_type": "execute_result" "output_type": "execute_result"
} }
@ -252,7 +254,7 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 18, "execution_count": 6,
"id": "2bbee865-c000-43da-84d9-ce7e04874110", "id": "2bbee865-c000-43da-84d9-ce7e04874110",
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [],
@ -271,7 +273,7 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 41, "execution_count": 20,
"id": "38eb4f87-ca3c-4ecf-a8ca-29422822d933", "id": "38eb4f87-ca3c-4ecf-a8ca-29422822d933",
"metadata": {}, "metadata": {},
"outputs": [ "outputs": [
@ -280,56 +282,56 @@
"output_type": "stream", "output_type": "stream",
"text": [ "text": [
"Training fold 0 for 20 epochs\n", "Training fold 0 for 20 epochs\n",
"Train samples:\t267\n", "\tTrain samples:\t267\n",
"Test samples:\t30\n", "\tTest samples:\t30\n",
"Accuracy of fold 0: 0.9\n", "\tAccuracy of fold 0: 0.8666666666666667\n",
"Training fold 1 for 20 epochs\n", "Training fold 1 for 20 epochs\n",
"Train samples:\t267\n", "\tTrain samples:\t267\n",
"Test samples:\t30\n", "\tTest samples:\t30\n",
"Accuracy of fold 1: 0.8666666666666667\n", "\tAccuracy of fold 1: 0.8\n",
"Training fold 2 for 20 epochs\n", "Training fold 2 for 20 epochs\n",
"Train samples:\t267\n", "\tTrain samples:\t267\n",
"Test samples:\t30\n", "\tTest samples:\t30\n",
"Accuracy of fold 2: 0.8666666666666667\n", "\tAccuracy of fold 2: 0.9\n",
"Training fold 3 for 20 epochs\n", "Training fold 3 for 20 epochs\n",
"Train samples:\t267\n", "\tTrain samples:\t267\n",
"Test samples:\t30\n", "\tTest samples:\t30\n",
"Accuracy of fold 3: 0.9\n", "\tAccuracy of fold 3: 0.9\n",
"Training fold 4 for 20 epochs\n", "Training fold 4 for 20 epochs\n",
"Train samples:\t267\n", "\tTrain samples:\t267\n",
"Test samples:\t30\n", "\tTest samples:\t30\n",
"Accuracy of fold 4: 0.9\n", "\tAccuracy of fold 4: 0.8666666666666667\n",
"Training fold 5 for 20 epochs\n", "Training fold 5 for 20 epochs\n",
"Train samples:\t267\n", "\tTrain samples:\t267\n",
"Test samples:\t30\n", "\tTest samples:\t30\n",
"Accuracy of fold 5: 0.8333333333333334\n", "\tAccuracy of fold 5: 0.8\n",
"Training fold 6 for 20 epochs\n", "Training fold 6 for 20 epochs\n",
"Train samples:\t267\n", "\tTrain samples:\t267\n",
"Test samples:\t30\n", "\tTest samples:\t30\n",
"Accuracy of fold 6: 0.7666666666666667\n", "\tAccuracy of fold 6: 0.8333333333333334\n",
"Training fold 7 for 20 epochs\n", "Training fold 7 for 20 epochs\n",
"Train samples:\t268\n", "\tTrain samples:\t268\n",
"Test samples:\t29\n", "\tTest samples:\t29\n",
"Accuracy of fold 7: 0.8275862068965517\n", "\tAccuracy of fold 7: 0.8620689655172413\n",
"Training fold 8 for 20 epochs\n", "Training fold 8 for 20 epochs\n",
"Train samples:\t268\n", "\tTrain samples:\t268\n",
"Test samples:\t29\n", "\tTest samples:\t29\n",
"Accuracy of fold 8: 0.7586206896551724\n", "\tAccuracy of fold 8: 0.7241379310344828\n",
"Training fold 9 for 20 epochs\n", "Training fold 9 for 20 epochs\n",
"Train samples:\t268\n", "\tTrain samples:\t268\n",
"Test samples:\t29\n", "\tTest samples:\t29\n",
"Accuracy of fold 9: 0.7586206896551724\n", "\tAccuracy of fold 9: 0.896551724137931\n",
"Avg accuracy 0.837816091954023\n" "Avg accuracy 0.8449425287356321\n"
] ]
} }
], ],
"source": [ "source": [
"from sklearn.model_selection import KFold\n",
"from sklearn import decomposition\n",
"import tensorflow as tf\n", "import tensorflow as tf\n",
"\n", "\n",
"use_pca = True\n",
"# number of components extracted from the pca\n", "# number of components extracted from the pca\n",
"n_features = 8\n", "n_features = 8\n",
"n_features = n_features if use_pca else len(X.columns)\n",
"\n", "\n",
"epochs = 20\n", "epochs = 20\n",
"k_folds = 10\n", "k_folds = 10\n",
@ -345,27 +347,30 @@
" X_train, X_test = X.iloc[train_idx], X.iloc[test_idx]\n", " X_train, X_test = X.iloc[train_idx], X.iloc[test_idx]\n",
" y_train, y_test = y[train_idx], y[test_idx]\n", " y_train, y_test = y[train_idx], y[test_idx]\n",
"\n", "\n",
" print(f'Train samples:\\t{len(X_train)}')\n", " print(f'\\tTrain samples:\\t{len(X_train)}')\n",
" print(f'Test samples:\\t{len(X_test)}')\n", " print(f'\\tTest samples:\\t{len(X_test)}')\n",
"\n", "\n",
" # do pca based on the train data of the given fold to extract 'n_features'\n", " if use_pca:\n",
" pca = decomposition.PCA(n_components=n_features)\n", " # do pca based on the train data of the given fold to extract 'n_features'\n",
" pca.fit(X_train)\n", " pca = decomposition.PCA(n_components=n_features)\n",
" X_train = pca.transform(X_train)\n", " pca.fit(X_train)\n",
" X_train = pca.transform(X_train)\n",
"\n", "\n",
" # train the model using the components extracted from pca\n", " # train the model using the components extracted from pca\n",
" model = get_model(n_features)\n", " model = get_model(n_features)\n",
" model.fit(X_train, y_train, epochs=epochs, verbose=0)\n", " model.fit(X_train, y_train, epochs=epochs, verbose=0)\n",
"\n", "\n",
" # transform test data using on the pca model trained on the train data\n", " if use_pca:\n",
" X_test = pca.transform(X_test)\n", " # transform test data using on the pca model trained on the train data\n",
" X_test = pca.transform(X_test)\n",
" \n",
" y_pred = model.predict(X_test, verbose=0)\n", " y_pred = model.predict(X_test, verbose=0)\n",
" y_pred = y_pred > 0.5\n", " y_pred = y_pred > 0.5 # threshold to binarize\n",
"\n", "\n",
" # calculate the accuracy of the train data for the current fold\n", " # calculate the accuracy of the train data for the current fold\n",
" accuracy = sum(y_pred == y_test)[0] / len(y_pred)\n", " accuracy = sum(y_pred == y_test)[0] / len(y_pred)\n",
" accuracies.append(accuracy)\n", " accuracies.append(accuracy)\n",
" print(f'Accuracy of fold {i}: {accuracy}')\n", " print(f'\\tAccuracy of fold {i}: {accuracy}')\n",
"\n", "\n",
"# calculate the average accuracy over all folds\n", "# calculate the average accuracy over all folds\n",
"avg_accuracy = sum(accuracies) / len(accuracies)\n", "avg_accuracy = sum(accuracies) / len(accuracies)\n",
@ -374,7 +379,7 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 42, "execution_count": 22,
"id": "95215693-47c9-4202-92f5-efbc65bc32c9", "id": "95215693-47c9-4202-92f5-efbc65bc32c9",
"metadata": {}, "metadata": {},
"outputs": [ "outputs": [
@ -383,16 +388,14 @@
"output_type": "stream", "output_type": "stream",
"text": [ "text": [
"Training fold 0 for 20 epochs\n", "Training fold 0 for 20 epochs\n",
"Train samples:\t237\n", "\tTrain samples:\t237\n",
"Test samples:\t60\n" "\tTest samples:\t60\n"
] ]
}, },
{ {
"name": "stderr", "name": "stderr",
"output_type": "stream", "output_type": "stream",
"text": [ "text": [
"C:\\Users\\maxwi\\anaconda3\\Lib\\site-packages\\sklearn\\cluster\\_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
" warnings.warn(\n",
"C:\\Users\\maxwi\\anaconda3\\Lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=1.\n", "C:\\Users\\maxwi\\anaconda3\\Lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=1.\n",
" warnings.warn(\n" " warnings.warn(\n"
] ]
@ -401,20 +404,16 @@
"name": "stdout", "name": "stdout",
"output_type": "stream", "output_type": "stream",
"text": [ "text": [
"[0 1 1 0 0 0 0 0 1 1 1 0 1 0 0 0 0 0 0 0 1 0 0 1 1 0 0 0 0 1 0 1 0 1 0 0 1\n", "\tAccuracy of fold 0: 0.5833333333333334\n",
" 1 1 1 1 1 0 0 0 1 0 1 0 0 0 1 0 0 1 1 1 1 0 1]\n",
"Accuracy of fold 0: 0.5833333333333334\n",
"Training fold 1 for 20 epochs\n", "Training fold 1 for 20 epochs\n",
"Train samples:\t237\n", "\tTrain samples:\t237\n",
"Test samples:\t60\n" "\tTest samples:\t60\n"
] ]
}, },
{ {
"name": "stderr", "name": "stderr",
"output_type": "stream", "output_type": "stream",
"text": [ "text": [
"C:\\Users\\maxwi\\anaconda3\\Lib\\site-packages\\sklearn\\cluster\\_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
" warnings.warn(\n",
"C:\\Users\\maxwi\\anaconda3\\Lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=1.\n", "C:\\Users\\maxwi\\anaconda3\\Lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=1.\n",
" warnings.warn(\n" " warnings.warn(\n"
] ]
@ -423,20 +422,16 @@
"name": "stdout", "name": "stdout",
"output_type": "stream", "output_type": "stream",
"text": [ "text": [
"[1 0 1 0 1 1 0 0 1 0 0 1 1 1 0 0 1 0 0 1 1 0 0 1 0 0 0 0 0 1 1 1 0 0 1 1 1\n", "\tAccuracy of fold 1: 0.5\n",
" 0 0 0 0 0 0 1 0 1 1 1 1 1 1 1 1 1 0 0 0 1 1 1]\n",
"Accuracy of fold 1: 0.5\n",
"Training fold 2 for 20 epochs\n", "Training fold 2 for 20 epochs\n",
"Train samples:\t238\n", "\tTrain samples:\t238\n",
"Test samples:\t59\n" "\tTest samples:\t59\n"
] ]
}, },
{ {
"name": "stderr", "name": "stderr",
"output_type": "stream", "output_type": "stream",
"text": [ "text": [
"C:\\Users\\maxwi\\anaconda3\\Lib\\site-packages\\sklearn\\cluster\\_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
" warnings.warn(\n",
"C:\\Users\\maxwi\\anaconda3\\Lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=1.\n", "C:\\Users\\maxwi\\anaconda3\\Lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=1.\n",
" warnings.warn(\n" " warnings.warn(\n"
] ]
@ -445,20 +440,16 @@
"name": "stdout", "name": "stdout",
"output_type": "stream", "output_type": "stream",
"text": [ "text": [
"[0 0 0 0 1 0 0 1 1 0 0 1 0 1 1 0 0 0 1 1 0 1 0 0 1 0 1 1 1 0 1 1 0 0 0 0 0\n", "\tAccuracy of fold 2: 0.559322033898305\n",
" 0 1 1 0 1 1 1 0 1 0 1 0 0 0 1 0 0 0 0 1 1 0]\n",
"Accuracy of fold 2: 0.559322033898305\n",
"Training fold 3 for 20 epochs\n", "Training fold 3 for 20 epochs\n",
"Train samples:\t238\n", "\tTrain samples:\t238\n",
"Test samples:\t59\n" "\tTest samples:\t59\n"
] ]
}, },
{ {
"name": "stderr", "name": "stderr",
"output_type": "stream", "output_type": "stream",
"text": [ "text": [
"C:\\Users\\maxwi\\anaconda3\\Lib\\site-packages\\sklearn\\cluster\\_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
" warnings.warn(\n",
"C:\\Users\\maxwi\\anaconda3\\Lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=1.\n", "C:\\Users\\maxwi\\anaconda3\\Lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=1.\n",
" warnings.warn(\n" " warnings.warn(\n"
] ]
@ -467,20 +458,16 @@
"name": "stdout", "name": "stdout",
"output_type": "stream", "output_type": "stream",
"text": [ "text": [
"[0 1 0 1 1 1 0 0 0 1 0 1 1 0 1 0 1 1 1 1 0 1 0 0 0 0 1 1 1 0 1 0 1 0 1 0 1\n", "\tAccuracy of fold 3: 0.576271186440678\n",
" 1 1 1 1 0 0 1 1 1 0 0 1 0 1 1 1 0 0 0 1 1 1]\n",
"Accuracy of fold 3: 0.576271186440678\n",
"Training fold 4 for 20 epochs\n", "Training fold 4 for 20 epochs\n",
"Train samples:\t238\n", "\tTrain samples:\t238\n",
"Test samples:\t59\n" "\tTest samples:\t59\n"
] ]
}, },
{ {
"name": "stderr", "name": "stderr",
"output_type": "stream", "output_type": "stream",
"text": [ "text": [
"C:\\Users\\maxwi\\anaconda3\\Lib\\site-packages\\sklearn\\cluster\\_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
" warnings.warn(\n",
"C:\\Users\\maxwi\\anaconda3\\Lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=1.\n", "C:\\Users\\maxwi\\anaconda3\\Lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=1.\n",
" warnings.warn(\n" " warnings.warn(\n"
] ]
@ -489,9 +476,7 @@
"name": "stdout", "name": "stdout",
"output_type": "stream", "output_type": "stream",
"text": [ "text": [
"[1 1 1 1 1 0 0 0 1 0 0 0 1 1 1 1 1 1 1 1 1 1 1 0 0 0 1 1 0 1 0 1 1 0 1 1 1\n", "\tAccuracy of fold 4: 0.5254237288135594\n",
" 1 0 1 0 1 0 0 0 1 1 0 1 0 0 0 1 0 0 0 0 0 1]\n",
"Accuracy of fold 4: 0.5254237288135594\n",
"Avg accuracy 0.5488700564971751\n" "Avg accuracy 0.5488700564971751\n"
] ]
} }
@ -499,6 +484,7 @@
"source": [ "source": [
"from sklearn.cluster import KMeans\n", "from sklearn.cluster import KMeans\n",
"\n", "\n",
"use_pca = True\n",
"# number of components extracted from the pca\n", "# number of components extracted from the pca\n",
"n_features = 10\n", "n_features = 10\n",
"\n", "\n",
@ -515,28 +501,29 @@
" X_train, X_test = X.iloc[train_idx], X.iloc[test_idx]\n", " X_train, X_test = X.iloc[train_idx], X.iloc[test_idx]\n",
" y_train, y_test = y[train_idx], y[test_idx]\n", " y_train, y_test = y[train_idx], y[test_idx]\n",
"\n", "\n",
" print(f'Train samples:\\t{len(X_train)}')\n", " print(f'\\tTrain samples:\\t{len(X_train)}')\n",
" print(f'Test samples:\\t{len(X_test)}')\n", " print(f'\\tTest samples:\\t{len(X_test)}')\n",
"\n", "\n",
" # do pca based on the train data of the given fold to extract 'n_features'\n", " if use_pca:\n",
" #pca = decomposition.PCA(n_components=n_features)\n", " # do pca based on the train data of the given fold to extract 'n_features'\n",
" #pca.fit(X_train)\n", " pca = decomposition.PCA(n_components=n_features)\n",
" #X_train = pca.transform(X_train)\n", " pca.fit(X_train)\n",
" X_train = pca.transform(X_train)\n",
"\n", "\n",
" model = KMeans(n_clusters=2)\n", " model = KMeans(n_clusters=2, n_init=10)\n",
" model.fit(X_train)\n", " model.fit(X_train)\n",
"\n", "\n",
" #X_test = pca.transform(X_test)\n", " if use_pca:\n",
" X_test = pca.transform(X_test)\n",
" \n",
" y_pred = model.predict(X_test)\n", " y_pred = model.predict(X_test)\n",
" print(y_pred)\n",
" \n",
"\n", "\n",
" # calculate the accuracy of the train data for the current fold\n", " # calculate the accuracy of the train data for the current fold\n",
" accuracy1 = sum(y_pred == y_test)[0] / len(y_pred)\n", " accuracy1 = sum(y_pred == y_test)[0] / len(y_pred)\n",
" accuracy2 = sum(y_pred != y_test)[0] / len(y_pred)\n", " accuracy2 = sum(y_pred != y_test)[0] / len(y_pred)\n",
" accuracy = max(accuracy1, accuracy2)\n", " accuracy = max(accuracy1, accuracy2)\n",
" accuracies.append(accuracy)\n", " accuracies.append(accuracy)\n",
" print(f'Accuracy of fold {i}: {accuracy}')\n", " print(f'\\tAccuracy of fold {i}: {accuracy}')\n",
"\n", "\n",
"# calculate the average accuracy over all folds\n", "# calculate the average accuracy over all folds\n",
"avg_accuracy = sum(accuracies) / len(accuracies)\n", "avg_accuracy = sum(accuracies) / len(accuracies)\n",
@ -545,11 +532,85 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": null, "execution_count": 23,
"id": "880302e4-82c1-47b9-9fe3-cb3567511639", "id": "880302e4-82c1-47b9-9fe3-cb3567511639",
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [
"source": [] {
"name": "stdout",
"output_type": "stream",
"text": [
"Training fold 0 for 20 epochs\n",
"\tTrain samples:\t237\n",
"\tTest samples:\t60\n",
"\tAccuracy of fold 0: 0.85\n",
"Training fold 1 for 20 epochs\n",
"\tTrain samples:\t237\n",
"\tTest samples:\t60\n",
"\tAccuracy of fold 1: 0.9\n",
"Training fold 2 for 20 epochs\n",
"\tTrain samples:\t238\n",
"\tTest samples:\t59\n",
"\tAccuracy of fold 2: 0.847457627118644\n",
"Training fold 3 for 20 epochs\n",
"\tTrain samples:\t238\n",
"\tTest samples:\t59\n",
"\tAccuracy of fold 3: 0.7627118644067796\n",
"Training fold 4 for 20 epochs\n",
"\tTrain samples:\t238\n",
"\tTest samples:\t59\n",
"\tAccuracy of fold 4: 0.7796610169491526\n",
"Avg accuracy 0.8279661016949152\n"
]
}
],
"source": [
"from sklearn.ensemble import RandomForestClassifier\n",
"\n",
"use_pca = True\n",
"# number of components extracted from the pca\n",
"n_features = 10\n",
"\n",
"k_folds = 5\n",
"\n",
"# used to split the dataset into k folds\n",
"kf = KFold(n_splits=k_folds)\n",
"\n",
"accuracies = []\n",
"for i, (train_idx, test_idx) in enumerate(kf.split(X[numeric_columns])):\n",
" print(f'Training fold {i} for {epochs} epochs')\n",
"\n",
" # extract train and test data from the cleaned dataset\n",
" X_train, X_test = X.iloc[train_idx], X.iloc[test_idx]\n",
" y_train, y_test = y[train_idx], y[test_idx]\n",
" y_train, y_test = y_train[:, 0], y_test[:, 0]\n",
"\n",
" print(f'\\tTrain samples:\\t{len(X_train)}')\n",
" print(f'\\tTest samples:\\t{len(X_test)}')\n",
"\n",
" if use_pca:\n",
" # do pca based on the train data of the given fold to extract 'n_features'\n",
" pca = decomposition.PCA(n_components=n_features)\n",
" pca.fit(X_train)\n",
" X_train = pca.transform(X_train)\n",
"\n",
" model = RandomForestClassifier(max_depth=2, random_state=0)\n",
" model.fit(X_train, y_train)\n",
"\n",
" if use_pca:\n",
" X_test = pca.transform(X_test)\n",
" \n",
" y_pred = model.predict(X_test)\n",
"\n",
" # calculate the accuracy of the train data for the current fold\n",
" accuracy = sum(y_pred == y_test) / len(y_pred)\n",
" accuracies.append(accuracy)\n",
" print(f'\\tAccuracy of fold {i}: {accuracy}')\n",
"\n",
"# calculate the average accuracy over all folds\n",
"avg_accuracy = sum(accuracies) / len(accuracies)\n",
"print(f'Avg accuracy {avg_accuracy}')"
]
} }
], ],
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