{"metadata":{"kernelspec":{"language":"python","display_name":"Python 3","name":"python3"},"language_info":{"name":"python","version":"3.12.12","mimetype":"text/x-python","codemirror_mode":{"name":"ipython","version":3},"pygments_lexer":"ipython3","nbconvert_exporter":"python","file_extension":".py"},"kaggle":{"accelerator":"none","dataSources":[{"sourceType":"competition","sourceId":3004,"databundleVersionId":861823}],"dockerImageVersionId":31286,"isInternetEnabled":true,"language":"python","sourceType":"notebook","isGpuEnabled":false}},"nbformat_minor":4,"nbformat":4,"cells":[{"cell_type":"code","source":"%matplotlib inline\nimport cv2\nimport numpy as np\nimport matplotlib.pyplot as plt","metadata":{"_uuid":"8f2839f25d086af736a60e9eeb907d3b93b6e0e5","_cell_guid":"b1076dfc-b9ad-4769-8c92-a6c4dae69d19","trusted":true,"execution":{"iopub.status.busy":"2026-03-14T08:21:22.607735Z","iopub.execute_input":"2026-03-14T08:21:22.608106Z","iopub.status.idle":"2026-03-14T08:21:22.941133Z","shell.execute_reply.started":"2026-03-14T08:21:22.608071Z","shell.execute_reply":"2026-03-14T08:21:22.940134Z"}},"outputs":[],"execution_count":1},{"cell_type":"code","source":"train = np.loadtxt('/kaggle/input/competitions/digit-recognizer/train.csv', delimiter=',', skiprows=1)\ntest = np.loadtxt('/kaggle/input/competitions/digit-recognizer/test.csv', delimiter=',', skiprows=1)","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2026-03-14T08:25:48.91808Z","iopub.execute_input":"2026-03-14T08:25:48.919097Z","iopub.status.idle":"2026-03-14T08:25:53.447063Z","shell.execute_reply.started":"2026-03-14T08:25:48.919054Z","shell.execute_reply":"2026-03-14T08:25:53.446176Z"}},"outputs":[],"execution_count":6},{"cell_type":"code","source":"# сохраняем разметку в отдельную переменную\ntrain_label = train[:, 0]\n# приводим размерность к удобному для обаботки виду\ntrain_img = np.resize(train[:, 1:], (train.shape[0], 28, 28))\ntest_img = np.resize(test, (test.shape[0], 28, 28))","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2026-03-14T08:26:01.79512Z","iopub.execute_input":"2026-03-14T08:26:01.795463Z","iopub.status.idle":"2026-03-14T08:26:02.048934Z","shell.execute_reply.started":"2026-03-14T08:26:01.795434Z","shell.execute_reply":"2026-03-14T08:26:02.048035Z"}},"outputs":[],"execution_count":7},{"cell_type":"code","source":"train_img.shape, test_img.shape","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2026-03-14T08:26:09.22425Z","iopub.execute_input":"2026-03-14T08:26:09.225115Z","iopub.status.idle":"2026-03-14T08:26:09.233972Z","shell.execute_reply.started":"2026-03-14T08:26:09.225063Z","shell.execute_reply":"2026-03-14T08:26:09.232975Z"}},"outputs":[{"execution_count":8,"output_type":"execute_result","data":{"text/plain":"((42000, 28, 28), (28000, 28, 28))"},"metadata":{}}],"execution_count":8},{"cell_type":"code","source":"fig = plt.figure(figsize=(20, 10))\nfor i, img in enumerate(train_img[0:5], 1):\n subplot = fig.add_subplot(1, 7, i)\n plt.imshow(img, cmap='gray');\n subplot.set_title('%s' % train_label[i - 1]);","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2026-03-14T08:26:14.53831Z","iopub.execute_input":"2026-03-14T08:26:14.538628Z","iopub.status.idle":"2026-03-14T08:26:15.191638Z","shell.execute_reply.started":"2026-03-14T08:26:14.538601Z","shell.execute_reply":"2026-03-14T08:26:15.190766Z"}},"outputs":[{"output_type":"display_data","data":{"text/plain":"
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= (28,28)\nblockSize = (8,8)\nblockStride = (4,4)\ncellSize = (4,4)\nnbins = 9\nderivAperture = 1\nwinSigma = 4.\nhistogramNormType = 0\nL2HysThreshold = 2.0000000000000001e-01\ngammaCorrection = 0\nnlevels = 64\n\nhog = cv2.HOGDescriptor(winSize,\n blockSize,\n blockStride,\n cellSize,\n nbins,\n derivAperture,\n winSigma,\n histogramNormType,\n L2HysThreshold,\n gammaCorrection,\n nlevels)","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2026-03-14T08:26:50.94703Z","iopub.execute_input":"2026-03-14T08:26:50.947785Z","iopub.status.idle":"2026-03-14T08:26:50.954824Z","shell.execute_reply.started":"2026-03-14T08:26:50.947755Z","shell.execute_reply":"2026-03-14T08:26:50.953895Z"}},"outputs":[],"execution_count":10},{"cell_type":"code","source":"descriptor = hog.compute(np.uint8(train_img[0]))\nprint('Descriptor size: %d' % len(descriptor))","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2026-03-14T08:26:59.477793Z","iopub.execute_input":"2026-03-14T08:26:59.478247Z","iopub.status.idle":"2026-03-14T08:26:59.495479Z","shell.execute_reply.started":"2026-03-14T08:26:59.478217Z","shell.execute_reply":"2026-03-14T08:26:59.494553Z"}},"outputs":[{"name":"stdout","text":"Descriptor size: 1296\n","output_type":"stream"}],"execution_count":11},{"cell_type":"code","source":"from matplotlib import pyplot as plt\n\nfig = plt.figure()\nax = fig.add_axes([0,0,1,1])\nax.bar(range(len(descriptor)), descriptor.reshape(-1))\nplt.show()","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2026-03-14T08:27:04.215008Z","iopub.execute_input":"2026-03-14T08:27:04.215344Z","iopub.status.idle":"2026-03-14T08:27:05.681292Z","shell.execute_reply.started":"2026-03-14T08:27:04.215315Z","shell.execute_reply":"2026-03-14T08:27:05.680399Z"}},"outputs":[{"output_type":"display_data","data":{"text/plain":"
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\n"},"metadata":{}}],"execution_count":12},{"cell_type":"code","source":"train_hog = np.array([hog.compute(np.uint8(img)).flatten() for img in train_img])\ntest_hog = np.array([hog.compute(np.uint8(img)).flatten() for img in test_img])","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2026-03-14T08:27:11.399197Z","iopub.execute_input":"2026-03-14T08:27:11.399525Z","iopub.status.idle":"2026-03-14T08:27:14.252225Z","shell.execute_reply.started":"2026-03-14T08:27:11.399497Z","shell.execute_reply":"2026-03-14T08:27:14.25122Z"}},"outputs":[],"execution_count":13},{"cell_type":"code","source":"from sklearn.ensemble import RandomForestClassifier\nfrom sklearn.model_selection import train_test_split\n\nX_train, X_val, y_train, y_val = train_test_split(train_hog, train_label, test_size=0.2, random_state=42, stratify=train_label)","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2026-03-14T08:27:17.26402Z","iopub.execute_input":"2026-03-14T08:27:17.264825Z","iopub.status.idle":"2026-03-14T08:27:19.280054Z","shell.execute_reply.started":"2026-03-14T08:27:17.264793Z","shell.execute_reply":"2026-03-14T08:27:19.279171Z"}},"outputs":[],"execution_count":14},{"cell_type":"code","source":"import optuna\nfrom sklearn.model_selection import cross_val_score\nfrom sklearn.metrics import accuracy_score\n\nall_matched = {}\nseed = 98\n\ndef objective(trial):\n model_params = {\n 'n_estimators': trial.suggest_int('n_estimators', 50, 300),\n 'max_depth': trial.suggest_int('max_depth', 5, 50),\n 'min_samples_split': trial.suggest_int('min_samples_split', 2, 20),\n 'random_state': seed,\n 'n_jobs': -1\n }\n\n#модель\n model = RandomForestClassifier(**model_params)\n score = cross_val_score(model, train_hog, train_label, cv=3, scoring='accuracy', n_jobs=-1).mean()\n\n all_matched.update(\n {\n score: {\n 'data': None,\n 'model': model,\n 'params': model_params\n }\n }\n )\n\n return score","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2026-03-14T08:27:25.625939Z","iopub.execute_input":"2026-03-14T08:27:25.626469Z","iopub.status.idle":"2026-03-14T08:27:26.259162Z","shell.execute_reply.started":"2026-03-14T08:27:25.626435Z","shell.execute_reply":"2026-03-14T08:27:26.258157Z"}},"outputs":[],"execution_count":15},{"cell_type":"code","source":"TRIALS = 3\nstudy = optuna.create_study(direction='maximize')\nstudy.optimize(objective, n_trials=TRIALS, show_progress_bar=True)\n\nbest_score = study.best_trial.value\nbest_model = all_matched[best_score]['model']\nbest_params = all_matched[best_score]['params']","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2026-03-14T08:27:32.416178Z","iopub.execute_input":"2026-03-14T08:27:32.416505Z","iopub.status.idle":"2026-03-14T08:34:28.405398Z","shell.execute_reply.started":"2026-03-14T08:27:32.416479Z","shell.execute_reply":"2026-03-14T08:34:28.404374Z"}},"outputs":[{"name":"stderr","text":"[I 2026-03-14 08:27:32,418] A new study created in memory with name: no-name-55305880-9710-407d-bacb-0ea8744558fb\n","output_type":"stream"},{"output_type":"display_data","data":{"text/plain":" 0%| | 0/3 [00:00#sk-container-id-1 {\n /* Definition of color scheme common for light and dark mode */\n --sklearn-color-text: #000;\n --sklearn-color-text-muted: #666;\n --sklearn-color-line: gray;\n /* Definition of color scheme for unfitted estimators */\n --sklearn-color-unfitted-level-0: #fff5e6;\n --sklearn-color-unfitted-level-1: #f6e4d2;\n --sklearn-color-unfitted-level-2: #ffe0b3;\n --sklearn-color-unfitted-level-3: chocolate;\n /* Definition of color scheme for fitted estimators */\n --sklearn-color-fitted-level-0: #f0f8ff;\n --sklearn-color-fitted-level-1: #d4ebff;\n --sklearn-color-fitted-level-2: #b3dbfd;\n --sklearn-color-fitted-level-3: cornflowerblue;\n\n /* Specific color for light theme */\n --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n --sklearn-color-icon: #696969;\n\n @media (prefers-color-scheme: dark) {\n /* Redefinition of color scheme for dark theme */\n --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n --sklearn-color-icon: #878787;\n }\n}\n\n#sk-container-id-1 {\n color: var(--sklearn-color-text);\n}\n\n#sk-container-id-1 pre {\n padding: 0;\n}\n\n#sk-container-id-1 input.sk-hidden--visually {\n border: 0;\n clip: rect(1px 1px 1px 1px);\n clip: rect(1px, 1px, 1px, 1px);\n height: 1px;\n margin: -1px;\n overflow: hidden;\n padding: 0;\n position: absolute;\n width: 1px;\n}\n\n#sk-container-id-1 div.sk-dashed-wrapped {\n border: 1px dashed var(--sklearn-color-line);\n margin: 0 0.4em 0.5em 0.4em;\n box-sizing: border-box;\n padding-bottom: 0.4em;\n background-color: var(--sklearn-color-background);\n}\n\n#sk-container-id-1 div.sk-container {\n /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n but bootstrap.min.css set `[hidden] { display: none !important; }`\n so we also need the `!important` here to be able to override the\n default hidden behavior on the sphinx rendered scikit-learn.org.\n See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n display: inline-block !important;\n position: relative;\n}\n\n#sk-container-id-1 div.sk-text-repr-fallback {\n display: none;\n}\n\ndiv.sk-parallel-item,\ndiv.sk-serial,\ndiv.sk-item {\n /* draw centered vertical line to link estimators */\n background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n background-size: 2px 100%;\n background-repeat: no-repeat;\n background-position: center center;\n}\n\n/* Parallel-specific style estimator block */\n\n#sk-container-id-1 div.sk-parallel-item::after {\n content: \"\";\n width: 100%;\n border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n flex-grow: 1;\n}\n\n#sk-container-id-1 div.sk-parallel {\n display: flex;\n align-items: stretch;\n justify-content: center;\n background-color: var(--sklearn-color-background);\n position: relative;\n}\n\n#sk-container-id-1 div.sk-parallel-item {\n display: flex;\n flex-direction: column;\n}\n\n#sk-container-id-1 div.sk-parallel-item:first-child::after {\n align-self: flex-end;\n width: 50%;\n}\n\n#sk-container-id-1 div.sk-parallel-item:last-child::after {\n align-self: flex-start;\n width: 50%;\n}\n\n#sk-container-id-1 div.sk-parallel-item:only-child::after {\n width: 0;\n}\n\n/* Serial-specific style estimator block */\n\n#sk-container-id-1 div.sk-serial {\n display: flex;\n flex-direction: column;\n align-items: center;\n background-color: var(--sklearn-color-background);\n padding-right: 1em;\n padding-left: 1em;\n}\n\n\n/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\nclickable and can be expanded/collapsed.\n- Pipeline and ColumnTransformer use this feature and define the default style\n- Estimators will overwrite some part of the style using the `sk-estimator` class\n*/\n\n/* Pipeline and ColumnTransformer style (default) */\n\n#sk-container-id-1 div.sk-toggleable {\n /* Default theme specific background. It is overwritten whether we have a\n specific estimator or a Pipeline/ColumnTransformer */\n background-color: var(--sklearn-color-background);\n}\n\n/* Toggleable label */\n#sk-container-id-1 label.sk-toggleable__label {\n cursor: pointer;\n display: flex;\n width: 100%;\n margin-bottom: 0;\n padding: 0.5em;\n box-sizing: border-box;\n text-align: center;\n align-items: start;\n justify-content: space-between;\n gap: 0.5em;\n}\n\n#sk-container-id-1 label.sk-toggleable__label .caption {\n font-size: 0.6rem;\n font-weight: lighter;\n color: var(--sklearn-color-text-muted);\n}\n\n#sk-container-id-1 label.sk-toggleable__label-arrow:before {\n /* Arrow on the left of the label */\n content: \"▸\";\n float: left;\n margin-right: 0.25em;\n color: var(--sklearn-color-icon);\n}\n\n#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {\n color: var(--sklearn-color-text);\n}\n\n/* Toggleable content - dropdown */\n\n#sk-container-id-1 div.sk-toggleable__content {\n max-height: 0;\n max-width: 0;\n overflow: hidden;\n text-align: left;\n /* unfitted */\n background-color: var(--sklearn-color-unfitted-level-0);\n}\n\n#sk-container-id-1 div.sk-toggleable__content.fitted {\n /* fitted */\n background-color: var(--sklearn-color-fitted-level-0);\n}\n\n#sk-container-id-1 div.sk-toggleable__content pre {\n margin: 0.2em;\n border-radius: 0.25em;\n color: var(--sklearn-color-text);\n /* unfitted */\n background-color: var(--sklearn-color-unfitted-level-0);\n}\n\n#sk-container-id-1 div.sk-toggleable__content.fitted pre {\n /* unfitted */\n background-color: var(--sklearn-color-fitted-level-0);\n}\n\n#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n /* Expand drop-down */\n max-height: 200px;\n max-width: 100%;\n overflow: auto;\n}\n\n#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n content: \"▾\";\n}\n\n/* Pipeline/ColumnTransformer-specific style */\n\n#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n color: var(--sklearn-color-text);\n background-color: var(--sklearn-color-unfitted-level-2);\n}\n\n#sk-container-id-1 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n background-color: var(--sklearn-color-fitted-level-2);\n}\n\n/* Estimator-specific style */\n\n/* Colorize estimator box */\n#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n /* unfitted */\n background-color: var(--sklearn-color-unfitted-level-2);\n}\n\n#sk-container-id-1 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n /* fitted */\n background-color: var(--sklearn-color-fitted-level-2);\n}\n\n#sk-container-id-1 div.sk-label label.sk-toggleable__label,\n#sk-container-id-1 div.sk-label label {\n /* The background is the default theme color */\n color: var(--sklearn-color-text-on-default-background);\n}\n\n/* On hover, darken the color of the background */\n#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {\n color: var(--sklearn-color-text);\n background-color: var(--sklearn-color-unfitted-level-2);\n}\n\n/* Label box, darken color on hover, fitted */\n#sk-container-id-1 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n color: var(--sklearn-color-text);\n background-color: var(--sklearn-color-fitted-level-2);\n}\n\n/* Estimator label */\n\n#sk-container-id-1 div.sk-label label {\n font-family: monospace;\n font-weight: bold;\n display: inline-block;\n line-height: 1.2em;\n}\n\n#sk-container-id-1 div.sk-label-container {\n text-align: center;\n}\n\n/* Estimator-specific */\n#sk-container-id-1 div.sk-estimator {\n font-family: monospace;\n border: 1px dotted var(--sklearn-color-border-box);\n border-radius: 0.25em;\n box-sizing: border-box;\n margin-bottom: 0.5em;\n /* unfitted */\n background-color: var(--sklearn-color-unfitted-level-0);\n}\n\n#sk-container-id-1 div.sk-estimator.fitted {\n /* fitted */\n background-color: var(--sklearn-color-fitted-level-0);\n}\n\n/* on hover */\n#sk-container-id-1 div.sk-estimator:hover {\n /* unfitted */\n background-color: var(--sklearn-color-unfitted-level-2);\n}\n\n#sk-container-id-1 div.sk-estimator.fitted:hover {\n /* fitted */\n background-color: var(--sklearn-color-fitted-level-2);\n}\n\n/* Specification for estimator info (e.g. \"i\" and \"?\") */\n\n/* Common style for \"i\" and \"?\" */\n\n.sk-estimator-doc-link,\na:link.sk-estimator-doc-link,\na:visited.sk-estimator-doc-link {\n float: right;\n font-size: smaller;\n line-height: 1em;\n font-family: monospace;\n background-color: var(--sklearn-color-background);\n border-radius: 1em;\n height: 1em;\n width: 1em;\n text-decoration: none !important;\n margin-left: 0.5em;\n text-align: center;\n /* unfitted */\n border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n color: var(--sklearn-color-unfitted-level-1);\n}\n\n.sk-estimator-doc-link.fitted,\na:link.sk-estimator-doc-link.fitted,\na:visited.sk-estimator-doc-link.fitted {\n /* fitted */\n border: var(--sklearn-color-fitted-level-1) 1pt solid;\n color: var(--sklearn-color-fitted-level-1);\n}\n\n/* On hover */\ndiv.sk-estimator:hover .sk-estimator-doc-link:hover,\n.sk-estimator-doc-link:hover,\ndiv.sk-label-container:hover .sk-estimator-doc-link:hover,\n.sk-estimator-doc-link:hover {\n /* unfitted */\n background-color: var(--sklearn-color-unfitted-level-3);\n color: var(--sklearn-color-background);\n text-decoration: none;\n}\n\ndiv.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n.sk-estimator-doc-link.fitted:hover,\ndiv.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n.sk-estimator-doc-link.fitted:hover {\n /* fitted */\n background-color: var(--sklearn-color-fitted-level-3);\n color: var(--sklearn-color-background);\n text-decoration: none;\n}\n\n/* Span, style for the box shown on hovering the info icon */\n.sk-estimator-doc-link span {\n display: none;\n z-index: 9999;\n position: relative;\n font-weight: normal;\n right: .2ex;\n padding: .5ex;\n margin: .5ex;\n width: min-content;\n min-width: 20ex;\n max-width: 50ex;\n color: var(--sklearn-color-text);\n box-shadow: 2pt 2pt 4pt #999;\n /* unfitted */\n background: var(--sklearn-color-unfitted-level-0);\n border: .5pt solid var(--sklearn-color-unfitted-level-3);\n}\n\n.sk-estimator-doc-link.fitted span {\n /* fitted */\n background: var(--sklearn-color-fitted-level-0);\n border: var(--sklearn-color-fitted-level-3);\n}\n\n.sk-estimator-doc-link:hover span {\n display: block;\n}\n\n/* \"?\"-specific style due to the `` HTML tag */\n\n#sk-container-id-1 a.estimator_doc_link {\n float: right;\n font-size: 1rem;\n line-height: 1em;\n font-family: monospace;\n background-color: var(--sklearn-color-background);\n border-radius: 1rem;\n height: 1rem;\n width: 1rem;\n text-decoration: none;\n /* unfitted */\n color: var(--sklearn-color-unfitted-level-1);\n border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n}\n\n#sk-container-id-1 a.estimator_doc_link.fitted {\n /* fitted */\n border: var(--sklearn-color-fitted-level-1) 1pt solid;\n color: var(--sklearn-color-fitted-level-1);\n}\n\n/* On hover */\n#sk-container-id-1 a.estimator_doc_link:hover {\n /* unfitted */\n background-color: var(--sklearn-color-unfitted-level-3);\n color: var(--sklearn-color-background);\n text-decoration: none;\n}\n\n#sk-container-id-1 a.estimator_doc_link.fitted:hover {\n /* fitted */\n background-color: var(--sklearn-color-fitted-level-3);\n}\n"},"metadata":{}}],"execution_count":17},{"cell_type":"code","source":"print('Accuracy: %s' % accuracy_score(y_val, model.predict(X_val)))","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2026-03-14T08:44:09.340989Z","iopub.execute_input":"2026-03-14T08:44:09.341363Z","iopub.status.idle":"2026-03-14T08:44:09.559232Z","shell.execute_reply.started":"2026-03-14T08:44:09.341335Z","shell.execute_reply":"2026-03-14T08:44:09.558086Z"}},"outputs":[{"name":"stdout","text":"Accuracy: 0.9797619047619047\n","output_type":"stream"}],"execution_count":18},{"cell_type":"code","source":"from sklearn.metrics import classification_report\nprint(classification_report(y_val, model.predict(X_val)))","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2026-03-14T08:44:18.269519Z","iopub.execute_input":"2026-03-14T08:44:18.269853Z","iopub.status.idle":"2026-03-14T08:44:18.473317Z","shell.execute_reply.started":"2026-03-14T08:44:18.269828Z","shell.execute_reply":"2026-03-14T08:44:18.472503Z"}},"outputs":[{"name":"stdout","text":" precision recall f1-score support\n\n 0.0 0.98 0.99 0.99 827\n 1.0 0.99 0.99 0.99 937\n 2.0 0.97 0.98 0.98 835\n 3.0 0.98 0.97 0.98 870\n 4.0 0.98 0.99 0.98 814\n 5.0 0.98 0.97 0.98 759\n 6.0 0.99 0.99 0.99 827\n 7.0 0.97 0.98 0.98 880\n 8.0 0.98 0.97 0.98 813\n 9.0 0.97 0.97 0.97 838\n\n accuracy 0.98 8400\n macro avg 0.98 0.98 0.98 8400\nweighted avg 0.98 0.98 0.98 8400\n\n","output_type":"stream"}],"execution_count":19},{"cell_type":"code","source":"from sklearn.metrics import confusion_matrix\nprint(confusion_matrix(y_val, model.predict(X_val)))","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2026-03-14T08:44:22.054558Z","iopub.execute_input":"2026-03-14T08:44:22.054903Z","iopub.status.idle":"2026-03-14T08:44:22.273119Z","shell.execute_reply.started":"2026-03-14T08:44:22.054877Z","shell.execute_reply":"2026-03-14T08:44:22.272104Z"}},"outputs":[{"name":"stdout","text":"[[822 3 0 0 0 0 2 0 0 0]\n [ 0 923 10 1 0 0 0 2 1 0]\n [ 2 2 821 2 2 0 0 4 1 1]\n [ 1 0 6 846 0 7 0 6 1 3]\n [ 0 1 2 0 805 0 1 1 0 4]\n [ 1 1 1 5 0 736 5 2 2 6]\n [ 2 0 0 0 0 4 817 0 4 0]\n [ 0 2 5 1 4 1 0 862 1 4]\n [ 7 0 2 3 3 0 2 0 788 8]\n [ 4 0 1 3 7 1 0 10 2 810]]\n","output_type":"stream"}],"execution_count":20},{"cell_type":"code","source":"pred_test=model.predict(test_hog)","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2026-03-14T08:44:24.168109Z","iopub.execute_input":"2026-03-14T08:44:24.168447Z","iopub.status.idle":"2026-03-14T08:44:24.812324Z","shell.execute_reply.started":"2026-03-14T08:44:24.168419Z","shell.execute_reply":"2026-03-14T08:44:24.811234Z"}},"outputs":[],"execution_count":21},{"cell_type":"code","source":"fig = plt.figure(figsize=(20, 10))\nfor i, img in enumerate(test_img[0:5], 1):\n subplot = fig.add_subplot(1, 7, i)\n plt.imshow(img, cmap='gray');\n subplot.set_title('%s' % pred_test[i - 1]);","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2026-03-14T08:44:25.57429Z","iopub.execute_input":"2026-03-14T08:44:25.574826Z","iopub.status.idle":"2026-03-14T08:44:26.16185Z","shell.execute_reply.started":"2026-03-14T08:44:25.574789Z","shell.execute_reply":"2026-03-14T08:44:26.160667Z"}},"outputs":[{"output_type":"display_data","data":{"text/plain":"
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