File size: 41,550 Bytes
0d00d62 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341 342 343 344 345 346 347 348 349 350 351 352 353 354 355 356 357 358 359 360 361 362 363 364 365 366 367 368 369 370 371 372 373 374 375 376 377 378 379 380 381 382 383 384 385 386 387 388 389 390 391 392 393 394 395 396 397 398 399 400 401 402 403 404 405 406 407 408 409 410 411 412 413 414 415 416 417 418 419 420 421 422 423 424 425 426 427 428 429 430 431 432 433 434 435 436 437 438 439 440 441 442 443 444 445 446 447 448 449 450 451 452 453 454 455 456 457 458 459 460 461 462 463 464 465 466 467 468 469 470 471 472 473 474 475 476 477 478 479 480 481 482 483 484 485 486 487 488 489 490 491 492 493 494 495 496 497 498 499 500 501 502 503 504 505 506 507 508 509 510 511 512 513 514 515 516 517 518 519 520 521 522 523 524 525 526 527 528 529 530 531 532 533 534 535 536 537 538 539 540 541 542 543 544 545 546 547 548 549 550 551 552 553 554 555 556 557 558 559 560 561 562 563 564 565 566 567 568 569 570 571 572 573 574 575 576 577 578 579 580 581 582 583 584 585 586 587 588 589 590 591 592 593 594 595 596 597 598 599 600 601 602 603 604 605 606 607 608 609 610 611 612 613 614 615 616 617 618 619 620 621 622 623 624 625 626 627 628 629 630 631 632 633 634 635 636 637 638 639 640 641 642 643 644 645 646 647 648 649 650 651 652 653 654 655 656 657 658 659 660 661 662 663 664 665 666 667 668 669 670 671 672 673 674 675 676 677 678 679 680 681 682 683 684 685 686 687 688 689 690 691 692 693 694 695 696 697 698 699 700 701 702 703 704 705 706 707 708 709 710 711 712 713 714 715 716 717 718 719 720 721 722 723 724 725 726 727 728 729 730 731 732 733 734 735 736 737 738 739 740 741 742 743 744 745 746 747 748 749 750 751 752 753 754 755 756 757 758 759 760 761 762 763 764 765 766 767 768 769 770 771 772 773 774 775 776 777 778 779 780 781 782 783 784 785 786 787 788 789 790 791 792 793 794 795 796 797 798 799 800 801 802 803 804 805 806 807 808 809 810 811 812 813 814 815 816 817 818 819 820 821 822 823 824 825 826 827 828 829 830 831 832 833 834 835 836 837 838 839 840 841 842 843 844 845 846 847 848 849 850 851 852 853 854 855 856 857 858 859 860 861 862 863 864 865 866 867 868 869 870 871 872 873 874 875 876 877 878 879 880 881 882 883 884 885 886 887 888 889 890 891 892 893 894 895 896 897 898 899 900 901 902 903 904 905 906 907 908 909 910 911 912 913 914 915 916 917 918 919 920 921 922 923 924 925 926 927 928 929 930 931 932 933 934 935 936 937 938 939 940 941 942 943 944 945 946 947 948 949 950 951 952 953 954 955 956 957 958 959 960 961 962 963 964 965 966 967 968 969 970 971 972 973 974 975 976 977 978 979 980 981 982 983 984 985 986 987 988 989 990 991 992 993 994 995 996 997 998 999 1000 1001 1002 1003 1004 1005 1006 1007 1008 1009 1010 1011 1012 1013 1014 1015 1016 1017 1018 1019 1020 1021 1022 1023 1024 1025 1026 1027 1028 1029 1030 1031 1032 1033 1034 1035 1036 1037 1038 1039 1040 1041 1042 1043 1044 1045 1046 1047 1048 1049 1050 1051 1052 1053 1054 1055 1056 1057 1058 1059 1060 1061 1062 1063 1064 1065 1066 1067 1068 1069 1070 1071 1072 1073 1074 1075 1076 1077 1078 1079 1080 1081 1082 1083 1084 1085 1086 1087 1088 1089 1090 1091 1092 1093 1094 1095 1096 1097 1098 1099 1100 1101 1102 1103 1104 1105 1106 1107 1108 1109 1110 1111 1112 1113 1114 1115 1116 1117 1118 1119 1120 1121 1122 1123 1124 1125 1126 1127 1128 1129 1130 1131 1132 1133 1134 1135 1136 1137 1138 1139 1140 1141 1142 1143 1144 1145 1146 1147 1148 1149 1150 1151 1152 1153 1154 1155 1156 1157 1158 1159 1160 1161 1162 1163 1164 1165 1166 1167 1168 1169 1170 1171 1172 1173 1174 1175 1176 1177 1178 1179 1180 1181 1182 1183 1184 1185 1186 1187 1188 1189 1190 1191 1192 1193 1194 1195 1196 1197 1198 1199 1200 1201 1202 1203 1204 1205 1206 1207 1208 |
{
"title": "Random Forest Mastery: 100 MCQs",
"description": "A comprehensive set of multiple-choice questions designed to test and deepen your understanding of Random Forest, covering fundamentals, parameters, ensemble concepts, and practical applications.",
"questions": [
{
"id": 1,
"questionText": "What is Random Forest primarily used for?",
"options": [
"Only Clustering",
"Only Time Series",
"Only Image Processing",
"Classification and Regression"
],
"correctAnswerIndex": 3,
"explanation": "Random Forest is a versatile ensemble method used for both classification and regression tasks."
},
{
"id": 2,
"questionText": "Random Forest is an example of which type of learning?",
"options": [
"Supervised Learning",
"Unsupervised Learning",
"Self-Supervised Learning",
"Reinforcement Learning"
],
"correctAnswerIndex": 0,
"explanation": "Random Forest is trained using labeled data, so it is supervised learning."
},
{
"id": 3,
"questionText": "What is the base algorithm used inside a Random Forest?",
"options": [
"Linear Regression",
"K-Means",
"Decision Trees",
"Neural Networks"
],
"correctAnswerIndex": 2,
"explanation": "Random Forest builds multiple Decision Trees and combines them."
},
{
"id": 4,
"questionText": "Why is it called 'Random' Forest?",
"options": [
"Because it gives random answers",
"Because trees are random shapes",
"Because it uses randomness in data and features",
"Because it is used randomly"
],
"correctAnswerIndex": 2,
"explanation": "Random Forest randomly selects data samples and features to build diverse trees."
},
{
"id": 5,
"questionText": "What does Random Forest reduce compared to a single Decision Tree?",
"options": [
"Accuracy",
"Computation Time",
"Overfitting",
"Data Size"
],
"correctAnswerIndex": 2,
"explanation": "By combining many trees, Random Forest reduces overfitting."
},
{
"id": 6,
"questionText": "What technique does Random Forest use to train different trees?",
"options": [
"Gradient Descent",
"Bootstrap Sampling",
"Dropout",
"Pooling"
],
"correctAnswerIndex": 1,
"explanation": "Random Forest uses bootstrap sampling (bagging) to create different training subsets."
},
{
"id": 7,
"questionText": "Random Forest is an example of which ensemble method?",
"options": [
"Boosting",
"Stacking",
"Bagging",
"Reinforcement"
],
"correctAnswerIndex": 2,
"explanation": "Random Forest is a bagging-based ensemble learning method."
},
{
"id": 8,
"questionText": "Which metric is commonly used to measure feature importance in Random Forest?",
"options": [
"Euclidean Distance",
"Entropy Loss",
"Gini Importance",
"Cosine Similarity"
],
"correctAnswerIndex": 2,
"explanation": "Gini Impurity is used to decide splits, and feature importance is derived from it."
},
{
"id": 9,
"questionText": "What does each individual tree in a Random Forest output during classification?",
"options": [
"A regression score only",
"A class prediction",
"A probability distribution",
"A clustering label"
],
"correctAnswerIndex": 1,
"explanation": "Each tree predicts a class, and Random Forest takes the majority vote."
},
{
"id": 10,
"questionText": "How does Random Forest make the final prediction in classification?",
"options": [
"Majority voting",
"Max pooling",
"Averaging",
"Sorting"
],
"correctAnswerIndex": 0,
"explanation": "Random Forest predicts the class with the highest number of votes from trees."
},
{
"id": 11,
"questionText": "What happens if we increase the number of trees in Random Forest?",
"options": [
"Accuracy usually improves",
"Model becomes unstable",
"Accuracy always decreases",
"It deletes trees randomly"
],
"correctAnswerIndex": 0,
"explanation": "More trees reduce variance and improve accuracy until a saturation point."
},
{
"id": 12,
"questionText": "What kind of data can Random Forest handle?",
"options": [
"Only numerical",
"Only text data",
"Both categorical and numerical",
"Only time series"
],
"correctAnswerIndex": 2,
"explanation": "Random Forest works well with mixed data types."
},
{
"id": 13,
"questionText": "Random Forest is robust to which problem?",
"options": [
"Large memory usage",
"Outliers",
"Class imbalance",
"Overfitting"
],
"correctAnswerIndex": 3,
"explanation": "Random Forest reduces overfitting by combining multiple trees."
},
{
"id": 14,
"questionText": "What is the default criterion for splitting nodes in Random Forest classification?",
"options": [
"MAE",
"Gini Impurity",
"MSE",
"Cosine Distance"
],
"correctAnswerIndex": 1,
"explanation": "Gini impurity is the default split criterion for classification."
},
{
"id": 15,
"questionText": "How does Random Forest handle missing values?",
"options": [
"It ignores all rows",
"It can handle them fairly well",
"It crashes immediately",
"It replaces them with zeros"
],
"correctAnswerIndex": 1,
"explanation": "Random Forest can handle missing values better than many algorithms."
},
{
"id": 16,
"questionText": "What is the advantage of Random Forest over a single Decision Tree?",
"options": [
"No training required",
"Always 100% accuracy",
"Higher accuracy",
"Less training time"
],
"correctAnswerIndex": 2,
"explanation": "Random Forest is more accurate than a single Decision Tree due to ensemble voting."
},
{
"id": 17,
"questionText": "What type of sampling is used in Random Forest?",
"options": [
"Sequential sampling",
"Sampling with replacement",
"K-fold only",
"Sampling without replacement"
],
"correctAnswerIndex": 1,
"explanation": "Random Forest uses bootstrap sampling, which is sampling with replacement."
},
{
"id": 18,
"questionText": "What does each tree in Random Forest learn from?",
"options": [
"Only 50% of all features",
"Only one class of data",
"A random subset of data",
"The entire dataset"
],
"correctAnswerIndex": 2,
"explanation": "Each tree is trained on different bootstrapped samples."
},
{
"id": 19,
"questionText": "What happens if the number of trees is too small?",
"options": [
"Model becomes overconfident",
"It increases memory usage too much",
"It always overfits",
"Model may underfit"
],
"correctAnswerIndex": 3,
"explanation": "Too few trees may result in underfitting and poor accuracy."
},
{
"id": 20,
"questionText": "Random Forest reduces variance by?",
"options": [
"Adding dropout",
"Averaging multiple trees",
"Increasing learning rate",
"Minimizing entropy"
],
"correctAnswerIndex": 1,
"explanation": "Averaging predictions reduces variance and improves generalization."
},
{
"id": 21,
"questionText": "What is the method used to combine predictions in Random Forest?",
"options": [
"Majority voting",
"Stacking",
"Gradient descent",
"Concatenation"
],
"correctAnswerIndex": 0,
"explanation": "Classification is done using majority vote."
},
{
"id": 22,
"questionText": "What happens during training if two trees see different features?",
"options": [
"They predict randomly",
"They become identical",
"They learn different patterns",
"They crash"
],
"correctAnswerIndex": 2,
"explanation": "Feature randomness ensures diverse learning across trees."
},
{
"id": 23,
"questionText": "Is Random Forest sensitive to feature scaling?",
"options": [
"Yes",
"Only for categorical features",
"Only for small datasets",
"No"
],
"correctAnswerIndex": 3,
"explanation": "Random Forest does not require normalization or scaling."
},
{
"id": 24,
"questionText": "Random Forest internally uses how many Decision Trees?",
"options": [
"Based on dataset size",
"User-defined number",
"Exactly 10",
"Always 1"
],
"correctAnswerIndex": 1,
"explanation": "The number of trees is set by the user using the 'n_estimators' parameter."
},
{
"id": 25,
"questionText": "Random Forest works well when the dataset is?",
"options": [
"Only with time series",
"Large with many features",
"Only with text data",
"Very small only"
],
"correctAnswerIndex": 1,
"explanation": "Random Forest performs well with high-dimensional and large datasets."
},
{
"id": 26,
"questionText": "What is the output of Random Forest for binary classification?",
"options": [
"Probability only",
"Only 1",
"Only 0",
"0 or 1"
],
"correctAnswerIndex": 3,
"explanation": "The final output is a class label like 0 or 1."
},
{
"id": 27,
"questionText": "What is 'n_estimators' in Random Forest?",
"options": [
"Number of features",
"Number of layers",
"Number of epochs",
"Number of trees"
],
"correctAnswerIndex": 3,
"explanation": "'n_estimators' defines how many Decision Trees to train."
},
{
"id": 28,
"questionText": "What happens if all trees in Random Forest agree?",
"options": [
"Model crashes",
"Accuracy drops",
"High confidence in prediction",
"It becomes regression"
],
"correctAnswerIndex": 2,
"explanation": "More agreement among trees increases prediction confidence."
},
{
"id": 29,
"questionText": "Which parameter controls the depth of trees in Random Forest?",
"options": [
"n_estimators",
"learning_rate",
"max_depth",
"n_clusters"
],
"correctAnswerIndex": 2,
"explanation": "max_depth controls how deep each tree can grow."
},
{
"id": 30,
"questionText": "What is a potential drawback of Random Forest?",
"options": [
"Cannot classify data",
"Needs feature scaling",
"High memory usage",
"Always underfits"
],
"correctAnswerIndex": 2,
"explanation": "Training many trees can consume large memory and computation."
},
{
"id": 31,
"questionText": "What is the main reason Random Forest performs well compared to a single tree?",
"options": [
"It removes features randomly",
"It increases bias intentionally",
"It uses deep neural layers",
"It averages multiple trees to reduce variance"
],
"correctAnswerIndex": 3,
"explanation": "Averaging multiple independent trees stabilizes the predictions and lowers overfitting."
},
{
"id": 32,
"questionText": "What does the term 'out-of-bag' (OOB) error mean in Random Forest?",
"options": [
"Training error on all data",
"Error on random subsets",
"Loss on test set only",
"Error on unseen samples not used in training trees"
],
"correctAnswerIndex": 3,
"explanation": "OOB error estimates model performance using samples not included in the bootstrap subset."
},
{
"id": 33,
"questionText": "How does Random Forest ensure diversity among trees?",
"options": [
"By pruning all trees equally",
"Using same random seed",
"Random sampling of data and features",
"Training all trees on same data"
],
"correctAnswerIndex": 2,
"explanation": "Bootstrapping and random feature selection introduce variation between trees."
},
{
"id": 34,
"questionText": "Which of the following parameters controls the number of features considered for splitting?",
"options": [
"min_samples_split",
"max_features",
"n_estimators",
"max_depth"
],
"correctAnswerIndex": 1,
"explanation": "max_features limits how many features are chosen at each split, encouraging diversity."
},
{
"id": 35,
"questionText": "What happens if 'max_features' is set to 1 in a Random Forest?",
"options": [
"Each tree becomes highly decorrelated",
"All trees are identical",
"Model becomes identical to a single tree",
"Training stops early"
],
"correctAnswerIndex": 0,
"explanation": "When only one feature is chosen at each split, trees are very different, improving ensemble strength."
},
{
"id": 36,
"questionText": "Which evaluation metric is best for imbalanced classification using Random Forest?",
"options": [
"Accuracy",
"F1-score",
"MSE",
"R²"
],
"correctAnswerIndex": 1,
"explanation": "F1-score balances precision and recall, making it ideal for imbalanced datasets."
},
{
"id": 37,
"questionText": "Random Forest handles overfitting better than a single decision tree mainly due to?",
"options": [
"Ensemble averaging",
"Deep pruning",
"More bias",
"Gradient descent"
],
"correctAnswerIndex": 0,
"explanation": "Averaging the outputs of multiple uncorrelated trees reduces overfitting."
},
{
"id": 38,
"questionText": "What is the typical relationship between bias and variance in Random Forest?",
"options": [
"High bias, low variance",
"Low bias, high variance",
"High bias, high variance",
"Low bias, low variance"
],
"correctAnswerIndex": 3,
"explanation": "Random Forest balances both bias and variance well due to its ensemble structure."
},
{
"id": 39,
"questionText": "In Random Forest, which trees are used to predict a test sample?",
"options": [
"Random subset of trees",
"Only first tree",
"All trees in the ensemble",
"Last tree only"
],
"correctAnswerIndex": 2,
"explanation": "Each tree contributes to prediction, and results are aggregated by majority voting."
},
{
"id": 40,
"questionText": "What is the purpose of 'random_state' in Random Forest?",
"options": [
"Increasing randomness",
"Feature selection",
"Reproducibility",
"Performance improvement"
],
"correctAnswerIndex": 2,
"explanation": "random_state ensures the same random sampling for consistent results."
},
{
"id": 41,
"questionText": "What is the role of 'min_samples_split' in Random Forest?",
"options": [
"Number of bootstrap samples",
"Total number of features used",
"Maximum leaf nodes allowed",
"Minimum number of samples required to split an internal node"
],
"correctAnswerIndex": 3,
"explanation": "It prevents splits when a node has too few samples, reducing overfitting."
},
{
"id": 42,
"questionText": "What is feature importance in Random Forest?",
"options": [
"A pruning factor",
"A clustering metric",
"A measure of data imbalance",
"A score showing how useful a feature is for prediction"
],
"correctAnswerIndex": 3,
"explanation": "Feature importance reflects how much each feature reduces impurity in trees."
},
{
"id": 43,
"questionText": "What technique is used by Random Forest to combine multiple tree outputs?",
"options": [
"Stacking",
"Boosting",
"Bagging",
"Dropout"
],
"correctAnswerIndex": 2,
"explanation": "Random Forest is based on bagging — bootstrap aggregation of decision trees."
},
{
"id": 44,
"questionText": "If Random Forest has too many trees, what is the likely result?",
"options": [
"Accuracy decreases",
"Overfitting increases",
"Computation cost increases",
"Model becomes unstable"
],
"correctAnswerIndex": 2,
"explanation": "After a certain number, adding trees only increases computation without much gain."
},
{
"id": 45,
"questionText": "Which parameter limits how deep a tree can grow?",
"options": [
"n_estimators",
"max_depth",
"criterion",
"max_features"
],
"correctAnswerIndex": 1,
"explanation": "max_depth sets the maximum depth, controlling model complexity."
},
{
"id": 46,
"questionText": "What is the main drawback of Random Forest in large datasets?",
"options": [
"Low accuracy",
"High computational cost",
"High bias",
"No randomness"
],
"correctAnswerIndex": 1,
"explanation": "Training hundreds of trees can be time-consuming for large datasets."
},
{
"id": 47,
"questionText": "Which of these can Random Forest NOT handle directly?",
"options": [
"Categorical data",
"Sequential time dependencies",
"Missing values",
"Large datasets"
],
"correctAnswerIndex": 1,
"explanation": "Random Forest doesn’t model time dependencies, so it's not ideal for time series."
},
{
"id": 48,
"questionText": "How is randomness introduced in Random Forest?",
"options": [
"Bootstrap sampling and random feature selection",
"Gradient descent",
"Batch normalization",
"Learning rate scheduling"
],
"correctAnswerIndex": 0,
"explanation": "Random Forest introduces randomness both in data and feature sampling."
},
{
"id": 49,
"questionText": "What type of ensemble method is Random Forest?",
"options": [
"Voting",
"Bagging",
"Boosting",
"Stacking"
],
"correctAnswerIndex": 1,
"explanation": "Random Forest uses bagging (bootstrap aggregation) to train multiple trees."
},
{
"id": 50,
"questionText": "What is the relationship between Decision Tree depth and overfitting?",
"options": [
"Deeper trees tend to overfit",
"Deeper trees always underfit",
"Depth has no effect",
"Shallow trees always overfit"
],
"correctAnswerIndex": 0,
"explanation": "Large tree depth can cause the model to memorize training data patterns."
},
{
"id": 51,
"questionText": "What happens to the Random Forest model if trees are too shallow?",
"options": [
"Model overfits",
"Training time increases",
"Variance increases",
"Model underfits"
],
"correctAnswerIndex": 3,
"explanation": "Shallow trees can't capture complex data patterns."
},
{
"id": 52,
"questionText": "Why does Random Forest not require feature scaling?",
"options": [
"It normalizes automatically",
"It splits based on thresholds, not distance",
"It uses Euclidean distance",
"It drops correlated features"
],
"correctAnswerIndex": 1,
"explanation": "Tree-based methods are invariant to feature scaling."
},
{
"id": 53,
"questionText": "What happens if all trees are trained on identical bootstrap samples?",
"options": [
"Higher accuracy",
"No effect",
"Reduced diversity",
"Faster training"
],
"correctAnswerIndex": 2,
"explanation": "Lack of randomness among trees reduces ensemble benefit."
},
{
"id": 54,
"questionText": "Which statement is TRUE about Random Forest?",
"options": [
"It removes all bias",
"It reduces bias but increases variance",
"It increases both bias and variance",
"It reduces variance but keeps bias low"
],
"correctAnswerIndex": 3,
"explanation": "Bagging in Random Forest reduces variance without significantly increasing bias."
},
{
"id": 55,
"questionText": "In Random Forest, what does 'bootstrap=True' mean?",
"options": [
"No randomness is applied",
"Each tree skips feature selection",
"All trees use the full dataset",
"Each tree is trained on a random sample with replacement"
],
"correctAnswerIndex": 3,
"explanation": "Bootstrap sampling ensures each tree sees a slightly different dataset."
},
{
"id": 56,
"questionText": "How is feature importance calculated in Random Forest?",
"options": [
"Based on learning rate",
"Using feature frequency",
"By gradient descent",
"Based on impurity reduction"
],
"correctAnswerIndex": 3,
"explanation": "It measures how much each feature decreases node impurity across all trees."
},
{
"id": 57,
"questionText": "What is a typical hyperparameter tuning technique for Random Forest?",
"options": [
"Grid Search or Random Search",
"K-means",
"Dropout",
"Gradient Descent"
],
"correctAnswerIndex": 0,
"explanation": "Both Grid and Random Search are popular for hyperparameter tuning."
},
{
"id": 58,
"questionText": "What happens if we set 'n_estimators' too high?",
"options": [
"Lower accuracy",
"Longer training time",
"Underfitting",
"Loss of randomness"
],
"correctAnswerIndex": 1,
"explanation": "Too many trees make training slow, though accuracy improvement becomes marginal."
},
{
"id": 59,
"questionText": "How is Random Forest resistant to overfitting?",
"options": [
"Using deeper trees",
"Gradient correction",
"Averaging independent trees",
"Removing bias"
],
"correctAnswerIndex": 2,
"explanation": "Averaging many independent models cancels out noise and variance."
},
{
"id": 60,
"questionText": "Which of the following best describes the Random Forest algorithm?",
"options": [
"A single large decision tree",
"Linear regression with trees",
"Stacked boosting method",
"Ensemble of decision trees trained on random subsets"
],
"correctAnswerIndex": 3,
"explanation": "Random Forest is an ensemble approach using bagging and random feature selection."
},
{
"id": 61,
"questionText": "What is the main reason Random Forest works well even with noisy data?",
"options": [
"It applies dropout regularization",
"It removes noise automatically",
"It memorizes noise across all trees",
"It averages multiple trees to smooth out noise"
],
"correctAnswerIndex": 3,
"explanation": "Averaging predictions of multiple trees reduces the impact of noise in data."
},
{
"id": 62,
"questionText": "Which technique helps Random Forest estimate generalization error without a validation set?",
"options": [
"Cross-validation only",
"Early stopping",
"Out-of-Bag (OOB) estimation",
"Dropout sampling"
],
"correctAnswerIndex": 2,
"explanation": "OOB samples are not seen during training, allowing internal error estimation."
},
{
"id": 63,
"questionText": "What is the effect of increasing 'min_samples_split' too much?",
"options": [
"Model may underfit",
"Model may overfit",
"Training crashes",
"Bias becomes zero"
],
"correctAnswerIndex": 0,
"explanation": "Larger 'min_samples_split' prevents deeper splits, reducing learning capacity."
},
{
"id": 64,
"questionText": "What is the typical output of Random Forest in binary classification?",
"options": [
"Always continuous output",
"Softmax score",
"Only probability",
"Majority class from all trees"
],
"correctAnswerIndex": 3,
"explanation": "Random Forest uses majority voting to decide final class."
},
{
"id": 65,
"questionText": "In Random Forest, what happens if we disable bootstrap sampling?",
"options": [
"All trees become identical",
"Each tree will see full dataset",
"Training becomes impossible",
"Feature importance cannot be calculated"
],
"correctAnswerIndex": 1,
"explanation": "bootstrap=False means no sampling, trees are trained on complete dataset."
},
{
"id": 66,
"questionText": "Which Random Forest parameter controls how many features a single split considers?",
"options": [
"min_samples_split",
"max_depth",
"max_features",
"n_estimators"
],
"correctAnswerIndex": 2,
"explanation": "Randomly selecting only 'max_features' at each split ensures diversity."
},
{
"id": 67,
"questionText": "Which situation is most ideal for using Random Forest?",
"options": [
"Low-dimensional time series",
"Fully labeled image datasets only",
"Continuous text data",
"High-dimensional structured tabular data"
],
"correctAnswerIndex": 3,
"explanation": "Random Forest is excellent for large structured numeric + categorical datasets."
},
{
"id": 68,
"questionText": "How does Random Forest improve generalization?",
"options": [
"By memorizing data patterns",
"By deep pruning all trees",
"By increasing bias",
"By reducing variance using averaging"
],
"correctAnswerIndex": 3,
"explanation": "Averaging predictions from many uncorrelated trees reduces variance."
},
{
"id": 69,
"questionText": "What is a scenario where Random Forest might perform poorly?",
"options": [
"Large tabular dataset",
"Handling missing values",
"Highly sequential time-based data",
"Text classification with manual encoding"
],
"correctAnswerIndex": 2,
"explanation": "Random Forest is not designed to understand sequential temporal dependencies."
},
{
"id": 70,
"questionText": "What is the advantage of using 'max_samples' parameter in Random Forest?",
"options": [
"It forces normalization",
"It increases tree depth",
"It controls how many samples each tree sees",
"It controls feature count"
],
"correctAnswerIndex": 2,
"explanation": "max_samples limits data per tree to improve speed and variability."
},
{
"id": 71,
"questionText": "Why is Random Forest called a 'bagging' technique?",
"options": [
"It merges deep networks",
"It sequentially boosts errors",
"It uses bootstrap sampling + aggregation",
"It stacks models layer by layer"
],
"correctAnswerIndex": 2,
"explanation": "Random Forest is based on Bagging = Bootstrap + Aggregation."
},
{
"id": 72,
"questionText": "What is the role of 'n_jobs' parameter in Random Forest?",
"options": [
"Controls parallel processing",
"Controls noise injection",
"Controls memory allocation",
"Controls feature removal"
],
"correctAnswerIndex": 0,
"explanation": "n_jobs defines how many CPU cores to use in training."
},
{
"id": 73,
"questionText": "What happens if trees in a Random Forest are highly correlated?",
"options": [
"Bias becomes zero",
"Performance decreases",
"No effect",
"Accuracy increases massively"
],
"correctAnswerIndex": 1,
"explanation": "Less diversity among trees means less benefit from ensemble averaging."
},
{
"id": 74,
"questionText": "Why is Random Forest naturally resistant to overfitting?",
"options": [
"Because it always uses shallow trees",
"Because it restricts learning",
"Because it averages predictions from multiple trees",
"Because it limits depth"
],
"correctAnswerIndex": 2,
"explanation": "Averaging predictions reduces variance and overfitting."
},
{
"id": 75,
"questionText": "What is the output of feature importance scores in Random Forest?",
"options": [
"Relative importance values per feature",
"Loss graph",
"Class probability distribution",
"Confusion matrix"
],
"correctAnswerIndex": 0,
"explanation": "Feature importance shows which features contribute most to splits."
},
{
"id": 76,
"questionText": "Which of these indicates Random Forest overfitting?",
"options": [
"High training accuracy, low test accuracy",
"Slow training time only",
"Equal train and test accuracy",
"Low training accuracy, high test accuracy"
],
"correctAnswerIndex": 0,
"explanation": "Overfitting means model fits training well but generalizes poorly."
},
{
"id": 77,
"questionText": "What is a good reason to increase 'min_samples_leaf'?",
"options": [
"To reduce bias",
"To force normalization",
"To reduce overfitting",
"To increase overfitting"
],
"correctAnswerIndex": 2,
"explanation": "Larger leaves generalize better by preventing overly specific splits."
},
{
"id": 78,
"questionText": "Which Random Forest parameter can reduce model size and computation?",
"options": [
"max_depth",
"All of the above",
"n_estimators",
"max_samples"
],
"correctAnswerIndex": 1,
"explanation": "Reducing number of trees, depth, or samples lowers computational load."
},
{
"id": 79,
"questionText": "Which part of Random Forest helps most against overfitting?",
"options": [
"Gradient correction",
"Feature normalization",
"Deep trees",
"Bagging"
],
"correctAnswerIndex": 3,
"explanation": "Bagging reduces variance by training trees independently on random subsets."
},
{
"id": 80,
"questionText": "What is the disadvantage of using very small 'max_depth' in Random Forest?",
"options": [
"Unbalanced samples",
"Memory leak",
"Overfitting",
"Underfitting"
],
"correctAnswerIndex": 3,
"explanation": "Very shallow trees cannot capture complex relationships."
},
{
"id": 81,
"questionText": "How does Random Forest handle feature correlation?",
"options": [
"It removes correlated features by default",
"It may give correlated features lower importance",
"It fails completely",
"It merges correlated features"
],
"correctAnswerIndex": 1,
"explanation": "If two features are correlated, importance may be split between them."
},
{
"id": 82,
"questionText": "What is 'Gini Importance' in Random Forest?",
"options": [
"Metric to find best cluster",
"Loss function for optimization",
"Error on OOB samples",
"Measure of how much a feature reduces impurity"
],
"correctAnswerIndex": 3,
"explanation": "It quantifies impurity reduction contributed by each feature."
},
{
"id": 83,
"questionText": "Why is Random Forest not ideal for time-series forecasting?",
"options": [
"It needs scaling",
"It ignores temporal order",
"It can't process numbers",
"It overfits too much"
],
"correctAnswerIndex": 1,
"explanation": "Random Forest treats data as independent samples, ignoring sequence dependence."
},
{
"id": 84,
"questionText": "What is a sign that 'n_estimators' should be increased?",
"options": [
"Very fast training",
"Perfect accuracy",
"High test variance",
"Low training accuracy only"
],
"correctAnswerIndex": 2,
"explanation": "Increasing trees reduces prediction variance and stabilizes model."
},
{
"id": 85,
"questionText": "What is 'entropy' used for in Random Forest?",
"options": [
"Learning rate control",
"Feature normalization",
"Pruning strategy",
"Split quality measure"
],
"correctAnswerIndex": 3,
"explanation": "Entropy and Gini are purity measures used to decide best splits."
},
{
"id": 86,
"questionText": "Which scenario may require reducing 'max_depth'?",
"options": [
"When training time is extremely short",
"When features are few",
"When training accuracy is perfect but test accuracy is low",
"When both accuracies are low"
],
"correctAnswerIndex": 2,
"explanation": "This indicates overfitting — reducing depth increases generalization."
},
{
"id": 87,
"questionText": "What is one major strength of Random Forest?",
"options": [
"Perfect for text generation",
"Robust to noise and overfitting",
"Predicts time trends",
"Always fastest model"
],
"correctAnswerIndex": 1,
"explanation": "Random Forest is sturdy against noisy data due to ensemble averaging."
},
{
"id": 88,
"questionText": "Increasing 'min_samples_leaf' will most likely:",
"options": [
"Make model generalize better",
"Decrease bias heavily",
"Increase training variance",
"Increase memorization"
],
"correctAnswerIndex": 0,
"explanation": "Larger leaves lead to simpler splits and better generalization."
},
{
"id": 89,
"questionText": "Which metric is best for class imbalance evaluation in Random Forest?",
"options": [
"MSE",
"Recall / F1-score",
"Accuracy only",
"R-squared"
],
"correctAnswerIndex": 1,
"explanation": "F1 handles imbalanced data better by balancing precision and recall."
},
{
"id": 90,
"questionText": "What happens if 'max_features' is too high?",
"options": [
"Lower training accuracy",
"Trees become more random",
"Trees become more similar",
"OOB error becomes undefined"
],
"correctAnswerIndex": 2,
"explanation": "More features → less randomness → higher correlation between trees."
},
{
"id": 91,
"questionText": "Which combination may indicate optimal Random Forest tuning?",
"options": [
"Low accuracy on both",
"High train accuracy, high test accuracy",
"Low train accuracy, high test accuracy",
"High train accuracy, low test accuracy"
],
"correctAnswerIndex": 1,
"explanation": "This indicates low bias and low variance — a well-generalized model."
},
{
"id": 92,
"questionText": "Why doesn’t Random Forest require much hyperparameter tuning compared to other models?",
"options": [
"It ignores input data",
"It is robust to overfitting and variance",
"It always needs deep tuning",
"It cannot be tuned"
],
"correctAnswerIndex": 1,
"explanation": "Random Forest naturally reduces variance and overfitting, making it less sensitive to hyperparameters."
},
{
"id": 93,
"questionText": "What is the effect of increasing 'n_estimators' on OOB error?",
"options": [
"OOB error is unaffected",
"OOB error fluctuates randomly",
"OOB error usually decreases and stabilizes",
"OOB error increases"
],
"correctAnswerIndex": 2,
"explanation": "More trees provide a better estimate of error and reduce variance of predictions."
},
{
"id": 94,
"questionText": "Which is true about correlated features in Random Forest?",
"options": [
"Correlation is ignored completely",
"Random Forest fails with correlation",
"Correlated features are removed automatically",
"Importance may be split among correlated features"
],
"correctAnswerIndex": 3,
"explanation": "When features are correlated, importance scores may be shared, lowering individual scores."
},
{
"id": 95,
"questionText": "Random Forest is considered a black-box model because?",
"options": [
"It outputs linear coefficients",
"It uses shallow trees only",
"It is hard to interpret individual predictions",
"It has only one tree"
],
"correctAnswerIndex": 2,
"explanation": "The ensemble of many trees makes it difficult to trace exact reasoning for predictions."
},
{
"id": 96,
"questionText": "Which is a good approach to reduce Random Forest computation on very large datasets?",
"options": [
"Remove bagging",
"Use all features",
"Increase depth",
"Reduce 'n_estimators' or use 'max_samples'"
],
"correctAnswerIndex": 3,
"explanation": "Fewer trees or smaller bootstrap samples lower computational cost."
},
{
"id": 97,
"questionText": "Why is Random Forest more stable than a single Decision Tree?",
"options": [
"Because it uses scaling",
"Because it prunes all trees heavily",
"Because it has only one tree",
"Because predictions are averaged over many trees"
],
"correctAnswerIndex": 3,
"explanation": "Averaging reduces sensitivity to noise and variance in data."
},
{
"id": 98,
"questionText": "What kind of bias-variance tradeoff does Random Forest achieve?",
"options": [
"High bias, low variance",
"Low bias, low variance",
"High bias, high variance",
"Low bias, high variance"
],
"correctAnswerIndex": 1,
"explanation": "Bagging ensures variance reduction while keeping bias relatively low."
},
{
"id": 99,
"questionText": "Which Random Forest feature allows quick insight into feature relevance?",
"options": [
"Feature importance scores",
"OOB error",
"min_samples_split",
"max_depth"
],
"correctAnswerIndex": 0,
"explanation": "These scores help identify which features are most influential in predictions."
},
{
"id": 100,
"questionText": "In Random Forest classification, which method aggregates the outputs of all trees?",
"options": [
"Gradient boosting",
"Weighted averaging",
"Softmax",
"Majority voting"
],
"correctAnswerIndex": 3,
"explanation": "Random Forest takes the class predicted by the majority of trees as the final output."
}
]
}
|