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An equivalent width (EW) based classification may cause the erroneousjudgement to the flat spectrum radio quasars (FSRQs) and BL Lacerate objects(BL Lac) due to the diluting the line features by dramatic variations in thejet continuum flux. To help address the issue, the present paper explore thepossible intrinsic classification on the bias of a random forest supervisedmachine learning algorithm. In order to do so, we compile a sample of 1680Fermi blazars that have both gamma-rays and radio-frequencies data availablefrom the 4LAC-DR2 catalog, which includes 1352 training and validation samplesand 328 forecast samples. By studying the results for all of the differentcombinations of 23 characteristic parameters, we found that there are 178optimal parameters combinations (OPCs) with the highest accuracy ($\simeq$98.89\%). Using the combined classification results from the nine combinationsof these OPCs to the 328 forecast samples, we predict that there are 113 trueBL Lacs (TBLs) and 157 false BL Lacs (FBLs) that are possible intrinsicallyFSRQs misclassified as BL Lacs. The FBLs show a clear separation from TBLs andFSRQs in the $\gamma$-ray photon spectral index, $\Gamma_{\rm ph}$, and X-bandradio flux, ${\rm log}{F_{R}}$, plot. Phenomenally, existence a BL Lac to FSRQ(B-to-F) transition zone is suggested, where the FBLs are in the stage oftransition from BL Lacs to FSRQs. Comparing the LSP Changing-Look Blazars(CLBs) reported in the literatures, the majority of LSP CLBs are located at theB-to-F zone. We argue that the FBLs located at B-to-F transition zone are themost likely Candidates of CLBs.

Hunting for the candidates of misclassified sources in LSP BL Lacs using Machine learning