airline.df<-read.csv(paste("SixAirlinesDataV2.csv", sep=""))
summary(airline.df)## Airline Aircraft FlightDuration TravelMonth
## AirFrance: 74 AirBus:151 Min. : 1.250 Aug:127
## British :175 Boeing:307 1st Qu.: 4.260 Jul: 75
## Delta : 46 Median : 7.790 Oct:127
## Jet : 61 Mean : 7.578 Sep:129
## Singapore: 40 3rd Qu.:10.620
## Virgin : 62 Max. :14.660
## IsInternational SeatsEconomy SeatsPremium PitchEconomy
## Domestic : 40 Min. : 78.0 Min. : 8.00 Min. :30.00
## International:418 1st Qu.:133.0 1st Qu.:21.00 1st Qu.:31.00
## Median :185.0 Median :36.00 Median :31.00
## Mean :202.3 Mean :33.65 Mean :31.22
## 3rd Qu.:243.0 3rd Qu.:40.00 3rd Qu.:32.00
## Max. :389.0 Max. :66.00 Max. :33.00
## PitchPremium WidthEconomy WidthPremium PriceEconomy
## Min. :34.00 Min. :17.00 Min. :17.00 Min. : 65
## 1st Qu.:38.00 1st Qu.:18.00 1st Qu.:19.00 1st Qu.: 413
## Median :38.00 Median :18.00 Median :19.00 Median :1242
## Mean :37.91 Mean :17.84 Mean :19.47 Mean :1327
## 3rd Qu.:38.00 3rd Qu.:18.00 3rd Qu.:21.00 3rd Qu.:1909
## Max. :40.00 Max. :19.00 Max. :21.00 Max. :3593
## PricePremium PriceRelative SeatsTotal PitchDifference
## Min. : 86.0 Min. :0.0200 Min. : 98 Min. : 2.000
## 1st Qu.: 528.8 1st Qu.:0.1000 1st Qu.:166 1st Qu.: 6.000
## Median :1737.0 Median :0.3650 Median :227 Median : 7.000
## Mean :1845.3 Mean :0.4872 Mean :236 Mean : 6.688
## 3rd Qu.:2989.0 3rd Qu.:0.7400 3rd Qu.:279 3rd Qu.: 7.000
## Max. :7414.0 Max. :1.8900 Max. :441 Max. :10.000
## WidthDifference PercentPremiumSeats
## Min. :0.000 Min. : 4.71
## 1st Qu.:1.000 1st Qu.:12.28
## Median :1.000 Median :13.21
## Mean :1.633 Mean :14.65
## 3rd Qu.:3.000 3rd Qu.:15.36
## Max. :4.000 Max. :24.69The main point of using the seed is to be able to reproduce a particular sequence of ‘random’ numbers. The seed number you choose is the starting point used in the generation of a sequence of random numbers, which is why (provided you use the same pseudo-random number generator) you’ll obtain the same results given the same seed number.
Splitting ratio: if (0<=splitratio<1)< code=“”> then SplitRatio fraction of points from Y will be set toTRUE if (SplitRatio==1) then one random point from Y will be set to TRUE if (SplitRatio>1) then SplitRatio number of points from Y will be set to TRUE
## Warning: package 'caTools' was built under R version 3.4.3## Airline Aircraft FlightDuration TravelMonth IsInternational
## 2 British Boeing 12.25 Aug International
## 15 British Boeing 11.58 Sep International
## 16 British Boeing 11.58 Oct International
## 19 British Boeing 9.16 Oct International
## 26 British Boeing 8.75 Aug International
## 28 British Boeing 8.75 Oct International
## 37 British Boeing 13.50 Sep International
## 42 British Boeing 3.83 Oct International
## 50 British Boeing 12.75 Sep International
## 53 British Boeing 11.08 Aug International
## 54 British Boeing 11.08 Sep International
## 55 British Boeing 11.08 Oct International
## 56 British Boeing 6.08 Aug International
## 61 British Boeing 12.50 Oct International
## 64 Virgin AirBus 8.00 Sep International
## 73 Virgin AirBus 7.75 Aug International
## 78 Delta Boeing 2.33 Oct Domestic
## 83 British Boeing 6.83 Aug International
## 85 British Boeing 6.83 Oct International
## 87 British Boeing 7.58 Sep International
## 94 Jet Boeing 3.08 Aug International
## 101 British AirBus 11.16 Sep International
## 102 British AirBus 11.16 Oct International
## 103 British AirBus 10.50 Jul International
## 108 British AirBus 13.08 Sep International
## 110 British AirBus 11.16 Sep International
## 114 British AirBus 3.58 Aug International
## 115 British AirBus 3.58 Sep International
## 119 British AirBus 3.58 Aug International
## 123 British AirBus 2.41 Aug International
## 124 British AirBus 3.25 Oct International
## 130 British AirBus 1.83 Aug International
## 144 British Boeing 1.25 Sep International
## 146 British AirBus 2.83 Oct International
## 151 British Boeing 1.33 Oct International
## 153 Delta Boeing 4.51 Aug Domestic
## 154 Delta Boeing 4.33 Jul Domestic
## 155 Delta Boeing 4.51 Jul Domestic
## 160 Virgin Boeing 12.08 Aug International
## 165 Virgin Boeing 9.91 Aug International
## 166 Virgin Boeing 9.91 Sep International
## 169 Virgin Boeing 10.83 Aug International
## 170 Virgin Boeing 10.83 Sep International
## 173 Virgin Boeing 12.58 Aug International
## 175 Virgin Boeing 10.75 Aug International
## 177 Virgin Boeing 10.75 Oct International
## 179 Virgin Boeing 11.33 Oct International
## 184 Virgin Boeing 12.58 Sep International
## 194 Virgin AirBus 6.91 Oct International
## 196 Virgin AirBus 6.58 Aug International
## 201 Virgin AirBus 10.41 Sep International
## 210 Virgin AirBus 7.75 Sep International
## 217 AirFrance AirBus 8.33 Sep International
## 218 AirFrance AirBus 8.33 Oct International
## 220 AirFrance AirBus 8.33 Aug International
## 221 AirFrance AirBus 8.33 Sep International
## 227 AirFrance AirBus 6.83 Oct International
## 231 AirFrance AirBus 8.08 Sep International
## 240 British Boeing 10.41 Aug International
## 246 British Boeing 9.91 Jul International
## 252 British Boeing 8.58 Oct International
## 261 British Boeing 8.66 Aug International
## 262 British Boeing 8.66 Sep International
## 265 British Boeing 7.25 Sep International
## 268 British Boeing 7.08 Aug International
## 270 British Boeing 7.08 Oct International
## 278 British Boeing 11.08 Sep International
## 283 Delta Boeing 4.63 Aug Domestic
## 284 Delta Boeing 4.65 Sep Domestic
## 285 Delta Boeing 4.66 Oct Domestic
## 286 Delta Boeing 4.70 Jul Domestic
## 289 Delta Boeing 4.36 Sep Domestic
## 296 Delta AirBus 1.95 Sep Domestic
## 297 Delta AirBus 2.26 Sep Domestic
## 298 Delta AirBus 2.55 Oct Domestic
## 313 Jet AirBus 9.50 Sep International
## 314 Jet AirBus 8.91 Aug International
## 320 Singapore Boeing 12.41 Sep International
## 321 Singapore Boeing 12.41 Oct International
## 325 Singapore Boeing 14.66 Aug International
## 331 Singapore Boeing 3.83 Jul International
## 333 Singapore Boeing 3.83 Sep International
## 336 Singapore Boeing 12.75 Sep International
## 342 AirFrance Boeing 7.50 Oct International
## 343 AirFrance Boeing 6.83 Aug International
## 353 AirFrance Boeing 9.50 Oct International
## 355 AirFrance Boeing 7.75 Aug International
## 358 AirFrance Boeing 9.41 Aug International
## 360 AirFrance Boeing 9.41 Oct International
## 364 AirFrance Boeing 11.91 Aug International
## 367 British Boeing 13.83 Aug International
## 370 British Boeing 13.33 Aug International
## 372 British Boeing 13.33 Oct International
## 377 British Boeing 9.58 Oct International
## 383 Jet Boeing 3.25 Oct International
## 389 Jet Boeing 2.50 Oct International
## 390 Jet Boeing 2.66 Jul International
## 396 Jet Boeing 4.16 Oct International
## 399 Jet Boeing 2.66 Aug International
## 405 Jet Boeing 3.25 Jul International
## 407 AirFrance Boeing 6.91 Oct International
## 410 Singapore AirBus 13.33 Jul International
## 418 Singapore AirBus 12.66 Jul International
## 423 Singapore AirBus 6.50 Aug International
## 424 Singapore AirBus 6.50 Sep International
## 426 AirFrance AirBus 13.00 Sep International
## 432 AirFrance Boeing 10.66 Oct International
## 435 AirFrance AirBus 8.50 Sep International
## 436 AirFrance Boeing 8.50 Oct International
## 441 Jet Boeing 3.16 Jul International
## 443 Jet Boeing 5.66 Jul International
## 444 Jet Boeing 5.66 Aug International
## 447 Jet Boeing 5.66 Jul International
## 450 Jet Boeing 2.58 Sep International
## 453 Jet Boeing 2.58 Jul International
## SeatsEconomy SeatsPremium PitchEconomy PitchPremium WidthEconomy
## 2 122 40 31 38 18
## 15 122 40 31 38 18
## 16 122 40 31 38 18
## 19 122 40 31 38 18
## 26 122 40 31 38 18
## 28 122 40 31 38 18
## 37 122 40 31 38 18
## 42 122 40 31 38 18
## 50 122 40 31 38 18
## 53 127 39 31 38 18
## 54 127 39 31 38 18
## 55 127 39 31 38 18
## 56 127 39 31 38 18
## 61 127 39 31 38 18
## 64 185 48 31 38 18
## 73 185 48 31 38 18
## 78 78 20 31 34 18
## 83 243 56 31 38 18
## 85 243 56 31 38 18
## 87 243 56 31 38 18
## 94 138 28 30 40 17
## 101 303 55 31 38 18
## 102 303 55 31 38 18
## 103 303 55 31 38 18
## 108 303 55 31 38 18
## 110 303 55 31 38 18
## 114 303 55 31 38 18
## 115 303 55 31 38 18
## 119 303 55 31 38 18
## 123 303 55 31 38 18
## 124 303 55 31 38 18
## 130 303 55 31 38 18
## 144 303 55 31 38 18
## 146 303 55 31 38 18
## 151 303 55 31 38 18
## 153 171 29 32 35 18
## 154 171 29 32 35 18
## 155 171 29 32 35 18
## 160 198 35 31 38 18
## 165 375 66 31 38 18
## 166 375 66 31 38 18
## 169 198 35 31 38 18
## 170 198 35 31 38 18
## 173 198 35 31 38 18
## 175 375 66 31 38 18
## 177 375 66 31 38 18
## 179 198 35 31 38 18
## 184 198 35 31 38 18
## 194 233 38 31 38 18
## 196 233 38 31 38 18
## 201 233 38 31 38 18
## 210 233 38 31 38 18
## 217 147 21 32 38 18
## 218 147 21 32 38 18
## 220 147 21 32 38 18
## 221 147 21 32 38 18
## 227 147 21 32 38 18
## 231 147 21 32 38 18
## 240 243 36 31 38 18
## 246 243 36 31 38 18
## 252 243 36 31 38 18
## 261 243 36 31 38 18
## 262 243 36 31 38 18
## 265 243 36 31 38 18
## 268 243 36 31 38 18
## 270 243 36 31 38 18
## 278 243 36 31 38 18
## 283 126 18 32 34 17
## 284 126 18 32 34 17
## 285 126 18 32 34 17
## 286 139 21 31 34 17
## 289 126 18 32 34 17
## 296 120 18 32 34 17
## 297 120 18 32 34 17
## 298 136 20 33 35 17
## 313 147 21 32 38 18
## 314 147 21 32 38 18
## 320 184 28 32 38 19
## 321 184 28 32 38 19
## 325 184 28 32 38 19
## 331 184 28 32 38 19
## 333 184 28 32 38 19
## 336 184 28 32 38 19
## 342 200 28 32 38 17
## 343 200 28 32 38 17
## 353 200 28 32 38 17
## 355 174 24 32 38 17
## 358 200 28 32 38 17
## 360 200 28 32 38 17
## 364 200 28 32 38 17
## 367 203 24 31 38 18
## 370 203 24 31 38 18
## 372 203 24 31 38 18
## 377 203 24 31 38 18
## 383 124 16 30 40 17
## 389 124 16 30 40 17
## 390 124 16 30 40 17
## 396 124 16 30 40 17
## 399 124 16 30 40 17
## 405 124 16 30 40 17
## 407 216 24 32 38 17
## 410 333 36 32 38 19
## 418 333 36 32 38 19
## 423 333 36 32 38 19
## 424 333 36 32 38 19
## 426 389 38 32 38 18
## 432 389 38 32 38 18
## 435 389 38 32 38 18
## 436 389 38 32 38 18
## 441 162 8 30 40 17
## 443 162 8 30 40 17
## 444 162 8 30 40 17
## 447 162 8 30 40 17
## 450 162 8 30 40 17
## 453 162 8 30 40 17
## WidthPremium PriceEconomy PricePremium PriceRelative SeatsTotal
## 2 19 2707 3725 0.38 162
## 15 19 1750 2656 0.52 162
## 16 19 1750 2656 0.52 162
## 19 19 1813 2504 0.38 162
## 26 19 1542 2084 0.35 162
## 28 19 1566 2084 0.33 162
## 37 19 940 1548 0.65 162
## 42 19 1224 1512 0.24 162
## 50 19 509 773 0.52 162
## 53 19 2156 2933 0.36 166
## 54 19 2156 2933 0.36 166
## 55 19 2156 2933 0.36 166
## 56 19 1634 2195 0.34 166
## 61 19 509 818 0.61 166
## 64 21 1813 3128 0.73 233
## 73 21 540 594 0.10 233
## 78 18 216 231 0.07 98
## 83 19 1444 2982 1.07 299
## 85 19 1444 2982 1.07 299
## 87 19 1824 2549 0.40 299
## 94 21 464 616 0.33 166
## 101 19 2384 3563 0.49 358
## 102 19 2384 3563 0.49 358
## 103 19 1848 3536 0.91 358
## 108 19 1758 2592 0.47 358
## 110 19 719 1634 1.27 358
## 114 19 402 442 0.10 358
## 115 19 402 442 0.10 358
## 119 19 322 348 0.08 358
## 123 19 276 306 0.11 358
## 124 19 249 285 0.14 358
## 130 19 201 237 0.18 358
## 144 19 109 141 0.30 358
## 146 19 97 125 0.29 358
## 151 19 65 86 0.33 358
## 153 18 423 467 0.10 200
## 154 18 483 527 0.09 200
## 155 18 713 757 0.06 200
## 160 21 1086 2964 1.73 233
## 165 21 1781 3509 0.97 441
## 166 21 1781 3509 0.97 441
## 169 21 1580 3019 0.91 233
## 170 21 1580 3019 0.91 233
## 173 21 1096 1710 0.56 233
## 175 21 2445 3694 0.51 441
## 177 21 2445 3694 0.51 441
## 179 21 2369 3540 0.49 233
## 184 21 1356 1710 0.26 233
## 194 21 1434 2982 1.08 271
## 196 21 1476 2997 1.03 271
## 201 21 1903 3509 0.84 271
## 210 21 540 594 0.10 271
## 217 19 2659 2859 0.08 168
## 218 19 2659 2859 0.08 168
## 220 19 2659 2859 0.08 168
## 221 19 2659 2859 0.08 168
## 227 19 2860 3063 0.07 168
## 231 19 2813 2922 0.04 168
## 240 19 1651 3509 1.13 279
## 246 19 2356 3200 0.36 279
## 252 19 1562 3099 0.98 279
## 261 19 1609 2292 0.42 279
## 262 19 1609 2292 0.42 279
## 265 19 1632 2278 0.40 279
## 268 19 1736 1866 0.07 279
## 270 19 1736 1866 0.07 279
## 278 19 1023 1199 0.17 279
## 283 17 363 407 0.12 144
## 284 17 363 407 0.12 144
## 285 17 363 407 0.12 144
## 286 17 413 457 0.11 160
## 289 17 413 457 0.11 144
## 296 17 166 181 0.09 138
## 297 17 329 354 0.08 138
## 298 17 243 262 0.08 156
## 313 19 661 928 0.40 168
## 314 19 676 931 0.38 168
## 320 20 1215 1947 0.60 212
## 321 20 1215 1947 0.60 212
## 325 20 1406 1584 0.13 212
## 331 20 563 619 0.10 212
## 333 20 563 619 0.10 212
## 336 20 1431 1564 0.09 212
## 342 19 2581 2781 0.08 228
## 343 19 2860 3063 0.07 228
## 353 19 3414 3524 0.03 228
## 355 19 3215 3325 0.03 198
## 358 19 3480 3589 0.03 228
## 360 19 3480 3589 0.03 228
## 364 19 3159 3243 0.03 228
## 367 19 3102 7414 1.39 227
## 370 19 2166 2470 0.14 227
## 372 19 2166 2470 0.14 227
## 377 19 524 797 0.52 227
## 383 21 149 398 1.67 140
## 389 21 118 267 1.26 140
## 390 21 108 228 1.11 140
## 396 21 156 318 1.04 140
## 399 21 127 228 0.79 140
## 405 21 594 696 0.17 140
## 407 19 648 1710 1.64 240
## 410 20 505 1004 0.99 369
## 418 20 690 1110 0.61 369
## 423 20 690 1110 0.61 369
## 424 20 690 1110 0.61 369
## 426 19 1522 3289 1.16 427
## 432 19 2996 3196 0.07 427
## 435 19 2979 3088 0.04 427
## 436 19 2979 3088 0.04 427
## 441 21 148 397 1.68 170
## 443 21 187 430 1.30 170
## 444 21 187 430 1.30 170
## 447 21 245 545 1.22 170
## 450 21 172 304 0.77 170
## 453 21 281 451 0.60 170
## PitchDifference WidthDifference PercentPremiumSeats
## 2 7 1 24.69
## 15 7 1 24.69
## 16 7 1 24.69
## 19 7 1 24.69
## 26 7 1 24.69
## 28 7 1 24.69
## 37 7 1 24.69
## 42 7 1 24.69
## 50 7 1 24.69
## 53 7 1 23.49
## 54 7 1 23.49
## 55 7 1 23.49
## 56 7 1 23.49
## 61 7 1 23.49
## 64 7 3 20.60
## 73 7 3 20.60
## 78 3 0 20.41
## 83 7 1 18.73
## 85 7 1 18.73
## 87 7 1 18.73
## 94 10 4 16.87
## 101 7 1 15.36
## 102 7 1 15.36
## 103 7 1 15.36
## 108 7 1 15.36
## 110 7 1 15.36
## 114 7 1 15.36
## 115 7 1 15.36
## 119 7 1 15.36
## 123 7 1 15.36
## 124 7 1 15.36
## 130 7 1 15.36
## 144 7 1 15.36
## 146 7 1 15.36
## 151 7 1 15.36
## 153 3 0 14.50
## 154 3 0 14.50
## 155 3 0 14.50
## 160 7 3 15.02
## 165 7 3 14.97
## 166 7 3 14.97
## 169 7 3 15.02
## 170 7 3 15.02
## 173 7 3 15.02
## 175 7 3 14.97
## 177 7 3 14.97
## 179 7 3 15.02
## 184 7 3 15.02
## 194 7 3 14.02
## 196 7 3 14.02
## 201 7 3 14.02
## 210 7 3 14.02
## 217 6 1 12.50
## 218 6 1 12.50
## 220 6 1 12.50
## 221 6 1 12.50
## 227 6 1 12.50
## 231 6 1 12.50
## 240 7 1 12.90
## 246 7 1 12.90
## 252 7 1 12.90
## 261 7 1 12.90
## 262 7 1 12.90
## 265 7 1 12.90
## 268 7 1 12.90
## 270 7 1 12.90
## 278 7 1 12.90
## 283 2 0 12.50
## 284 2 0 12.50
## 285 2 0 12.50
## 286 3 0 13.13
## 289 2 0 12.50
## 296 2 0 13.04
## 297 2 0 13.04
## 298 2 0 12.82
## 313 6 1 12.50
## 314 6 1 12.50
## 320 6 1 13.21
## 321 6 1 13.21
## 325 6 1 13.21
## 331 6 1 13.21
## 333 6 1 13.21
## 336 6 1 13.21
## 342 6 2 12.28
## 343 6 2 12.28
## 353 6 2 12.28
## 355 6 2 12.12
## 358 6 2 12.28
## 360 6 2 12.28
## 364 6 2 12.28
## 367 7 1 10.57
## 370 7 1 10.57
## 372 7 1 10.57
## 377 7 1 10.57
## 383 10 4 11.43
## 389 10 4 11.43
## 390 10 4 11.43
## 396 10 4 11.43
## 399 10 4 11.43
## 405 10 4 11.43
## 407 6 2 10.00
## 410 6 1 9.76
## 418 6 1 9.76
## 423 6 1 9.76
## 424 6 1 9.76
## 426 6 1 8.90
## 432 6 1 8.90
## 435 6 1 8.90
## 436 6 1 8.90
## 441 10 4 4.71
## 443 10 4 4.71
## 444 10 4 4.71
## 447 10 4 4.71
## 450 10 4 4.71
## 453 10 4 4.71NUll deviation=deviation of model with nothing but intercept Residual deviation=deviation of overall model with independent variables, hence lower degrees of freedom, difference in degrees=no of independent variables
model<-glm(Aircraft~.-TravelMonth, data=train, family = binomial) #all_parameters_except_travel_month## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurredsummary(model)##
## Call:
## glm(formula = Aircraft ~ . - TravelMonth, family = binomial,
## data = train)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -1.65903 -0.48889 0.00001 0.32806 2.80871
##
## Coefficients: (3 not defined because of singularities)
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -6.545e+02 7.207e+05 -0.001 0.99928
## AirlineBritish -1.347e+01 1.975e+04 -0.001 0.99946
## AirlineDelta -8.807e+01 5.460e+04 -0.002 0.99871
## AirlineJet -1.705e+02 7.700e+04 -0.002 0.99823
## AirlineSingapore 5.617e+00 2.715e+04 0.000 0.99983
## AirlineVirgin -6.523e+01 4.876e+04 -0.001 0.99893
## FlightDuration 1.588e-01 8.808e-02 1.803 0.07144 .
## IsInternationalInternational -2.842e+02 1.137e+05 -0.002 0.99801
## SeatsEconomy 1.867e-02 1.074e-02 1.738 0.08213 .
## SeatsPremium -2.340e-01 7.437e-02 -3.146 0.00166 **
## PitchEconomy -2.057e+01 1.975e+04 -0.001 0.99917
## PitchPremium 4.165e+01 2.655e+04 0.002 0.99875
## WidthEconomy -2.568e+01 1.224e+04 -0.002 0.99833
## WidthPremium 2.455e+01 2.978e+04 0.001 0.99934
## PriceEconomy 2.384e-03 1.401e-03 1.702 0.08880 .
## PricePremium -1.255e-03 8.830e-04 -1.421 0.15535
## PriceRelative 1.639e+00 1.109e+00 1.478 0.13937
## SeatsTotal NA NA NA NA
## PitchDifference NA NA NA NA
## WidthDifference NA NA NA NA
## PercentPremiumSeats 4.605e-01 2.057e-01 2.239 0.02515 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 438.92 on 342 degrees of freedom
## Residual deviance: 194.33 on 325 degrees of freedom
## AIC: 230.33
##
## Number of Fisher Scoring iterations: 22p = exp(??o + ??(x)) / exp(??o + ??(x)) + 1 = e^(??o + ??(Age)) / e^(??o + ??(x)) + 1 q=1-p=probability of failure odds ratio=p/(1-p) logit(p)=log(p/(1-p))=??o + ??(x), here p=dependent variable like y Regression model finds best fit values for coefficients to ensure linear relationship between logit(y) and x. type=‘response’ returns p or y; by default it returns logit(p) or y of linear regression
predict<-predict(model, type='response', newdata=test)## Warning in predict.lm(object, newdata, se.fit, scale = 1, type =
## ifelse(type == : prediction from a rank-deficient fit may be misleadingpredict## 2 15 16 19 26
## 9.973313e-01 9.939776e-01 9.939776e-01 9.921042e-01 9.900494e-01
## 28 37 42 50 53
## 9.902874e-01 9.938380e-01 9.733575e-01 9.909502e-01 9.942894e-01
## 54 55 56 61 64
## 9.942894e-01 9.942894e-01 9.822685e-01 9.892484e-01 3.078632e-01
## 73 78 83 85 87
## 1.496081e-01 1.000000e+00 4.714092e-01 4.714092e-01 5.880661e-01
## 94 101 102 103 108
## 1.000000e+00 7.184494e-01 7.184494e-01 5.686158e-01 7.180336e-01
## 110 114 115 119 123
## 6.605312e-01 1.524975e-01 1.524975e-01 1.393497e-01 1.177114e-01
## 124 130 144 146 151
## 1.335676e-01 1.106709e-01 1.112283e-01 1.356021e-01 1.138214e-01
## 153 154 155 160 165
## 1.000000e+00 1.000000e+00 1.000000e+00 6.599607e-01 1.936220e-02
## 166 169 170 173 175
## 1.936220e-02 5.571866e-01 5.571866e-01 6.041045e-01 3.937910e-02
## 177 179 184 194 196
## 3.937910e-02 7.002070e-01 6.343409e-01 2.839932e-01 2.733379e-01
## 201 210 217 218 220
## 4.242463e-01 1.776512e-01 1.168365e-01 1.168365e-01 1.168365e-01
## 221 227 231 240 246
## 1.168365e-01 1.136446e-01 1.370666e-01 9.151911e-01 9.571441e-01
## 252 261 262 265 268
## 8.951653e-01 9.140646e-01 9.140646e-01 8.984445e-01 9.150729e-01
## 270 278 283 284 285
## 9.150729e-01 9.099647e-01 8.073080e-01 8.078015e-01 8.080479e-01
## 286 289 296 297 298
## 1.000000e+00 8.068743e-01 7.126952e-01 7.526672e-01 1.000000e+00
## 313 314 320 321 325
## 2.220446e-16 2.220446e-16 7.397726e-01 7.397726e-01 8.238587e-01
## 331 333 336 342 343
## 2.640567e-01 2.640567e-01 7.787856e-01 1.000000e+00 1.000000e+00
## 353 355 358 360 364
## 1.000000e+00 1.000000e+00 1.000000e+00 1.000000e+00 1.000000e+00
## 367 370 372 377 383
## 9.476092e-01 9.913306e-01 9.913306e-01 9.503076e-01 1.000000e+00
## 389 390 396 399 405
## 1.000000e+00 1.000000e+00 1.000000e+00 1.000000e+00 1.000000e+00
## 407 410 418 423 424
## 1.000000e+00 6.551036e-01 5.549211e-01 3.191924e-01 3.191924e-01
## 426 432 435 436 441
## 2.025119e-02 8.272700e-02 6.279678e-02 6.279678e-02 1.000000e+00
## 443 444 447 450 453
## 1.000000e+00 1.000000e+00 1.000000e+00 1.000000e+00 1.000000e+00Airbus is false and Boeing is true. Hence, 96 times where Airbus was there was predicted correvtly while 20 times in which Airbus was there was predicted wrongly. TRUE POSITIVE=PREDICTED TRUE CORRECTLY TRUE NEGATIVE=PREDICTED FALSE CORRECTLY Cannot use table directly as table[1,1] as table already inbuilt function and cannot be used as object internally, which leads to closure. c=Accuracy=(TRUE POSITIVE+TRUE NEGATIVE)/(TP+TN+FN+FP) pre=Precision=(TRUE POSITIVE)/(TP+TN) re=recall=TP/(TP+FN)=TPR=Sensitivity spe=TNR=Specificity=(TN)/(TN+FP)
summary(test$Aircraft)## AirBus Boeing
## 35 80t<-table(test$Aircraft, predict>0.5)
t##
## FALSE TRUE
## AirBus 25 10
## Boeing 12 68t[1,1]## [1] 25acc=(t[1,1]+t[2,2])/sum(t)
acc## [1] 0.8086957pre=t[2,2]/(t[1,2]+t[2,2])
pre## [1] 0.8717949re=t[2,2]/(t[2,2]+t[2,1])
re## [1] 0.85spe=t[1,1]/(t[1,1]+t[1,2])
spe## [1] 0.7142857Note that the echo = FALSE parameter was added to the code chunk to prevent printing of the R code that generated the plot.