diff --git a/components/brave_perf_predictor/browser/bandwidth_linreg_parameters.h b/components/brave_perf_predictor/browser/bandwidth_linreg_parameters.h
index a626b02fc16c..5e348a5525d7 100644
--- a/components/brave_perf_predictor/browser/bandwidth_linreg_parameters.h
+++ b/components/brave_perf_predictor/browser/bandwidth_linreg_parameters.h
@@ -16,166 +16,165 @@
 
 namespace brave_perf_predictor {
 
-constexpr double model_intercept = 5.085562016132402;
-constexpr int feature_count = 214;
+constexpr double model_intercept = 5.085407773814489;
+constexpr int feature_count = 213;
 constexpr std::array<double, feature_count> model_coefficients = {
-0.12352367722740858,
--0.0,
+0.12337659866050482,
 -0.0,
 0.0,
 0.0,
 0.0,
-0.006892384968665546,
--0.0053006306015809974,
-0.012808044375561328,
--0.022568426204584803,
+0.006894977221883717,
+-0.0053057834130837605,
+0.012862385567785137,
+-0.022571575512130217,
 0.0,
-0.031262883891716595,
--0.008895304473470696,
+0.03128111390815784,
+-0.008905984853438976,
 0.0,
--0.012542033549705674,
--0.018165453504801546,
--0.008479435850304733,
+-0.012493676378740588,
+-0.01817560552389195,
+-0.008378072321895977,
 0.0,
 0.0,
-0.002311112946961536,
-0.04901700232714016,
+0.002283231356917989,
+0.048919735990114545,
 0.0,
 0.0,
 -0.0,
-0.03701018507587659,
-0.014184085455013776,
+0.03710665420979194,
+0.014291396463246602,
 -0.0,
-0.10265583719234812,
-0.05237925617436799,
+0.102892550977104,
+0.052414725498173366,
 0.0,
-0.2561434657008117,
-0.3954099722860754,
-0.4241724431436048,
-0.2554360194633748,
+0.25621205523724505,
+0.39537140102916657,
+0.42410309202890667,
+0.25546300017617013,
 -0.0,
-0.1110185928151764,
+0.11108001260714759,
 0.0,
 0.0,
 -0.0,
-0.369424591785374,
--0.0621249125494256,
+0.369347502104488,
+-0.0621473488525576,
 0.0,
--0.19227234809590055,
-0.009790817504584938,
+-0.19225865475037224,
+0.009684176926811635,
 -0.0,
-0.5981006783511988,
+0.5984031539555243,
 0.0,
 -0.0,
 0.0,
 0.0,
 -0.0,
--0.154225297347611,
-0.5815994580458169,
-0.07673442247118749,
+-0.15442052414735336,
+0.5819796534516541,
+0.07669089364860023,
 -0.0,
-0.07985643438092534,
+0.07996581319924465,
 0.0,
-0.08301654084812561,
-0.27974978085336505,
--0.3135774687124175,
+0.08300572314082845,
+0.27985657360814103,
+-0.31342877799575186,
 0.0,
 0.0,
 0.0,
 -0.0,
-0.21721484909061123,
+0.21725044259235132,
 -0.0,
 0.0,
--0.6603212899417499,
-0.47382998205049726,
+-0.6601745015855838,
+0.47381119601381766,
 0.0,
-0.031566994679697126,
+0.03181202307277254,
 0.0,
 -0.0,
 0.0,
 0.0,
 0.0,
-0.41987279273153577,
+0.41982354308978265,
 -0.0,
 -0.0,
 0.0,
--0.15704804281369258,
-0.05136354590905017,
+-0.15701236163909485,
+0.05143595038841564,
 -0.0,
--1.3967861171544764,
-0.012049836970958124,
--0.16190555154009043,
+-1.3967255076899114,
+0.012164951411760265,
+-0.16190436401823916,
 0.0,
-0.32236468110744876,
--0.10807182142138586,
+0.3220696681595943,
+-0.1082526947395474,
 0.0,
-0.28113499778312084,
-0.347455093844389,
+0.2810643037362584,
+0.3475914458610097,
 0.0,
 -0.0,
-0.21577214682210177,
+0.2159865469721421,
 -0.0,
 0.0,
--0.3494463283215819,
--0.15598019593473778,
-0.2937099798752649,
+-0.34943241394679975,
+-0.15571843423959625,
+0.2937142943175603,
 -0.0,
 -0.0,
--0.6670081562284095,
-0.03155616836841969,
+-0.6666677255779397,
+0.03158187579337792,
 -0.0,
 -0.0,
 -0.0,
--0.4821830747864856,
--0.1060109448038188,
+-0.4825339029883909,
+-0.10536520325606542,
 0.0,
 0.0,
-0.18025872807868917,
+0.18019166627132638,
 -0.0,
-0.059651718175110925,
-0.15412094403273932,
+0.060628635086959023,
+0.1535326725086174,
 0.0,
 0.0,
-0.4104485276968183,
--0.0,
+0.41012738584685204,
 -0.0,
--0.0573642521280568,
--0.0183387630905243,
+-3.9714104351330406e-06,
+-0.054919851303674425,
+-0.02084871656373664,
 -0.0,
-0.08825537070503531,
+0.08831247903148058,
 -0.0,
-0.3576615077317695,
+0.3576186594301512,
 -0.0,
-0.20454045632867554,
+0.2045708428966265,
 -0.0,
--0.22669760740718553,
-0.3391651045388519,
-0.40841054414438194,
-3.320355416478889e-05,
+-0.22633684673764687,
+0.33920619628350673,
+0.4087551349593832,
+0.00014400861713193862,
 -0.0,
-0.009846843806251981,
-0.6879938701965732,
--0.09809945092041333,
+0.009788614358444537,
+0.6882150688542139,
+-0.09819510616132636,
 0.0,
 0.0,
 0.0,
-0.24660181712712595,
+0.24647318516426506,
 0.0,
 0.0,
-0.2971937705828685,
+0.2974896639283871,
 -0.0,
-0.00042832988368958795,
-0.0011714853122736216,
-0.3681970714031466,
+0.1767238117388548,
+0.0971655540387981,
+0.09612967228805114,
 0.0,
--0.4757811965402313,
--0.48264457834902585,
+-0.47559667034732106,
+-0.48262999096637776,
 -0.0,
--0.14897936787284055,
+-0.14856884527780762,
 -0.0,
 0.0,
 0.0,
--0.06864524030396776,
+-0.06867861932624512,
 0.0,
 0.0,
 0.0,
@@ -184,63 +183,62 @@ constexpr std::array<double, feature_count> model_coefficients = {
 -0.0,
 -0.0,
 -0.0,
--0.0939514698836343,
--0.17375737654279771,
-0.4280298407194896,
-0.07921243395532432,
+-0.07016021046322078,
+-0.19673925717707294,
+0.4279422735214434,
+0.07935762516999385,
 0.0,
 0.0,
-0.002297138241541185,
+0.001748304149647706,
 -0.0,
 -0.0,
 -0.0,
--0.01300337447750894,
+-0.01305037928240179,
 -0.0,
-0.0643992239129706,
-0.19270927069891977,
--0.40965167510194395,
+0.2558178171151348,
+0.0013887823536121427,
+-0.4098203655723224,
 -0.0,
 -0.0,
--0.12522557448112873,
+-0.1250453331155213,
 0.0,
 0.0,
-0.07914189703224955,
-0.030383389451194256,
+0.10399781691346788,
+0.005453211270251716,
 -0.0,
-0.03990905584174161,
-1.157147484387678,
+0.03981426371078261,
+1.157031326454923,
 0.0,
 -0.0,
 0.0,
 0.0,
-0.013087405412848738,
+0.4259520667147177,
 -0.0,
-0.17464108300530065,
-0.25197880082746205,
+6.557171791471991e-07,
+0.014181325019371424,
 0.0,
--0.09217632122309656,
+-0.09220432982116501,
 0.0,
 0.0,
 0.0,
 -0.0,
 -0.0,
--0.13491648330155384,
+-0.1103248978017494,
 -0.0,
--0.03688074309361575,
--0.058518504088209695,
+-0.03628699126870285,
+-0.08325838279176839,
 0.0,
 -0.0,
--0.03592820829466529,
--0.3346781157893913,
+-0.3706138376948898,
+-2.0791965507275406e-05,
 -0.0
 };
 
-constexpr unsigned int standardise_feat_count = 24;
+constexpr unsigned int standardise_feat_count = 23;
 
 constexpr std::array<double, standardise_feat_count> standardise_feat_means = {
 11.155786350148368,
 1016.2856083086053,
-1902.963649851632,
 1181.3004451038576,
 717.0712166172107,
 1987.0066765578636,
@@ -267,7 +265,6 @@ constexpr std::array<double, standardise_feat_count> standardise_feat_means = {
 constexpr std::array<double, standardise_feat_count> standardise_feat_scale = {
 12.073811227476089,
 926.9436100765363,
-1793.6185415841003,
 979.2250013928962,
 779.8742124298378,
 1919.1239982290276,
@@ -294,7 +291,6 @@ constexpr std::array<double, standardise_feat_count> standardise_feat_scale = {
 const std::array<std::string, feature_count> feature_sequence{
     "adblockRequests",
     "metrics.firstMeaningfulPaint",
-    "metrics.interactive",
     "metrics.observedDomContentLoaded",
     "metrics.observedFirstVisualChange",
     "metrics.observedLoad",
@@ -718,10 +714,6 @@ const base::flat_map<std::string, stdfactor> stdfactor_map = {
     "metrics.firstMeaningfulPaint",
     { 1016.2856083086053, 926.9436100765363 }
   },
-  {
-    "metrics.interactive",
-    { 1902.963649851632, 1793.6185415841003 }
-  },
   {
     "metrics.observedDomContentLoaded",
     { 1181.3004451038576, 979.2250013928962 }
diff --git a/components/brave_perf_predictor/browser/bandwidth_linreg_unittest.cc b/components/brave_perf_predictor/browser/bandwidth_linreg_unittest.cc
index c729a74a8aa6..f137f30fc68a 100644
--- a/components/brave_perf_predictor/browser/bandwidth_linreg_unittest.cc
+++ b/components/brave_perf_predictor/browser/bandwidth_linreg_unittest.cc
@@ -19,7 +19,7 @@ TEST(BraveSavingsPredictorTest, FeatureArrayGetsPrediction) {
 TEST(BraveSavingsPredictorTest, HandlesSpecificVectorExample) {
   // This test needs to be updated for any change in the model
   constexpr std::array<double, feature_count> sample = {
-      20, 129, 225, 225, 142, 925,    5, 34662, 3,  317818,  9,  1702888,
+      20, 129, 225, 142, 925,    5, 34662, 3,  317818,  9,  1702888,
       0,  0,   1,   324, 32,  238315, 9, 90131, 54, 2367498, 59, 2384138,
       0,  1,   0,   0,   0,   0,      0, 0,     0,  1,       0,  0,
       0,  0,   1,   0,   0,   0,      0, 0,     0,  0,       0,  0,
@@ -77,7 +77,6 @@ TEST(BraveSavingsPredictorTest, HandesSpecificFeaturemapExample) {
       {"thirdParties.Po.st.blocked", 1},
       {"adblockRequests", 20},
       {"metrics.firstMeaningfulPaint", 129},
-      {"metrics.interactive", 225},
       {"metrics.observedDomContentLoaded", 225},
       {"metrics.observedFirstVisualChange", 142},
       {"metrics.observedLoad", 925},
diff --git a/components/brave_perf_predictor/python/config.py b/components/brave_perf_predictor/python/config.py
index f3dff71e0f15..3ca97235a5d9 100644
--- a/components/brave_perf_predictor/python/config.py
+++ b/components/brave_perf_predictor/python/config.py
@@ -20,7 +20,7 @@
     'feature_selector__k': 'all',
     'model__alpha': 0.00025,
     'model__fit_intercept': True,
-    'model__max_iter': 20000,
+    'model__max_iter': 10000,
     'model__normalize': True,
     'model__selection': 'random',
     'pre_processor__standardise__with_mean': True,
@@ -31,11 +31,11 @@
     'pre_processor__standardise__with_mean': [True],
     'pre_processor__standardise__with_std': [True],
     # 'pre_processor__num_cols_processor__polynomials__degree': [1],
-    'feature_selector__k': [200, 'all'],
+    'feature_selector__k': ['all'],
     # 'feature_selector__threshold': [0.0001, 0.001, 0.00001, 0],
     'model__alpha': [0.0005, 0.00025, 0.0001],
     'model__normalize': [True],
-    'model__max_iter': [10000, 20000],
+    'model__max_iter': [10000, 20000, 30000],
     'model__fit_intercept': [True],
     'model__selection': ['random']
 }
diff --git a/components/brave_perf_predictor/python/model.py b/components/brave_perf_predictor/python/model.py
index b7db96338e21..06cb3966b5e0 100644
--- a/components/brave_perf_predictor/python/model.py
+++ b/components/brave_perf_predictor/python/model.py
@@ -36,7 +36,7 @@ def _load_dataset(path):
     # Load data and reset index
     df = pd.read_csv(path, index_col='url', sep='\t')
     df = df.reset_index(drop=True)
-    df = df.drop(columns=['experiment'])
+    df = df.drop(columns=['experiment', 'metrics.interactive'])
 
     # Log transform target
     df['adblockSummary.wastedBytes_target_log10'] = np.log10(